Matheson Ramsey
ramml003
Using an ontology in place of flat data for Sequential Pattern
Mining
A minor thesis for the degree of
Bachelor of Computer Science (Honours)
School of Computer and Information Science
University of South Australia
25/10/2010
Supervisor
Jan Stanek
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Table of Contents
Glossary ................................................................................................................................................... v
Abstract .................................................................................................................................................. vi
1 Introduction ......................................................................................................................................... 1
1.1 Motivation..................................................................................................................................... 3
1.2 Research Question ........................................................................................................................ 3
2 Literature Review ................................................................................................................................. 3
2.1 Data Mining in Health Informatics ................................................................................................ 3
2.2 Sequential Pattern Mining ............................................................................................................ 5
2.3 Drug Ontologies ............................................................................................................................ 6
2.4 Electronic Health Records ............................................................................................................. 7
3 Methodology ........................................................................................................................................ 8
3.1 Raw data ....................................................................................................................................... 8
3.1.1 Ethical Considerations............................................................................................................ 8
3.1.2 Data summary ........................................................................................................................ 8
3.2 Pre-processing............................................................................................................................... 9
3.3 Sequential Pattern Mining (SPM)................................................................................................ 10
3.3.1 Algorithm summary ............................................................................................................. 11
3.3.2 Sequential Pattern Mining process ...................................................................................... 12
4 Results ................................................................................................................................................ 13
4.1 Output Format ............................................................................................................................ 13
4.2 Flat Data Pattern Mining Results ................................................................................................ 14
4.3 Ontology Data Pattern Mining Results ....................................................................................... 14
5 Discussion........................................................................................................................................... 16
5.1 Results Analysis ........................................................................................................................... 16
5.2 Using ontologies.......................................................................................................................... 17
5.3 Interestingness ............................................................................................................................ 18
5.4 Further implications .................................................................................................................... 19
6 Future Work ....................................................................................................................................... 19
6.1 Therapeutic pathways................................................................................................................. 19
6.2 Changing Granularity dynamically .............................................................................................. 20
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6.3 Exploring Interestingness ............................................................................................................ 20
6.4 Applying to other fields............................................................................................................... 20
7 Conclusion .......................................................................................................................................... 21
8 References ......................................................................................................................................... 21
9 Project Timeline ................................................................................................................................. 24
Appendix A – Ethics Approval Application ............................................................................................ 25
Appendix B – Results............................................................................................................................. 32
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Table of Figures
Figure 1: a prescription pathway ............................................................................................................ 1
Figure 2: the ATC drug classification model and an example for Propicillin .......................................... 2
Figure 3: the program process flow ........................................................................................................ 8
Figure 4: re-coding dosage information.................................................................................................. 9
Figure 5: using the WHOCC online ATC index to obtain ATC codes ..................................................... 10
Figure 6: preparing the prescription pathways for the sequential pattern mining .............................. 11
Figure 7: generating patterns by testing candidates against support thresholds ................................ 12
Figure 8: combining 2-item patterns into 3-item candidates, and then halting because no 3-item
patterns are discovered ........................................................................................................................ 12
Figure 9: example output of pattern mining program.......................................................................... 13
Figure 10: a summary for a stage of pattern mining ............................................................................ 13
Figure 11: a summary of the patterns discovered for the flat data ..................................................... 14
Figure 12: a comparison of number of 2-length patterns found at each level of ATC ontology .......... 15
Figure 13: the ATC level 1 patterns ....................................................................................................... 15
Figure 14: an example of dilution ......................................................................................................... 16
Figure 15: an example of contamination .............................................................................................. 16
Figure 16: an unusual pattern found from pattern mining .................................................................. 17
Figure 17: a prescription pathway and its equivalent therapeutic pathway ........................................ 19
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Glossary
Pathway
Clinical Pathway (CP)
Prescription Pathway
(PP)
Tuple
Flat data
Node
Hierarchy
Ontology
Granularity
Contamination
Dilution
Pattern
Sequential Pattern
Mining (SPM)
Interestingness
A sequence of drug prescriptions over time
A pathway that has been designed by medical professionals to be followed
in order to treat a certain condition or disease
The pathway as seen by the prescriber. It shows what drugs were
prescribed in what order; it does not involve which drugs are being
consumed at the same time, or how long prescriptions last for
A single item in a dataset; one row of values
A set of data for which all attributes are numeric or categorical; no
hierarchical objects, objects within objects, etc.
A single item that links to others. If a node links to another node it is that
node’s parent. If a node is linked to by another node it is that node’s child
A data structure that has multiple levels of nodes.
A hierarchy with a strict “is-a” relationship between parent and child levels
of nodes
The level of the hierarchy that is being referred to. A lower granularity
implies working higher up in the ontology (less granular being less specific),
while a high granularity implies working lower down in the ontology (more
granular being more specific)
Using a level of the hierarchy that is too high, so there are too many
unrelated subgroups included, and the pattern loses its meaning
Using a level of the hierarchy that is too low, so information is too specific,
and patterns may not emerge
A sequence of prescriptions that occurs often in the dataset
The process of extracting patterns from the dataset
The degree to which a pattern can be considered interesting or meaningful
v
Abstract
This paper investigates the impact of using an ontology in place of flat data on sequential pattern
mining. Prescription data is modelled in an ontology using the ATC drug classification. Sequential
pattern mining is performed by a new algorithm developed for this study, based on AprioriAll. The
algorithm searches for patterns across a sequential database, as well as within each sequence, and
uses 2 support thresholds to ensure meaningful results are obtained. The research shows that
introducing granularity by the use of an ontology does increase the amount of patterns found, and
the paper also explores some of the effects of using data abstraction such as contamination and
dilution. The paper summarises by observing that whilst there is an increase in pattern discovery as
the prescription data abstracts further up the ontology, this does not necessarily reflect a gain in
information from the database.
Field of thesis
Health informatics; Data Mining: Sequential Pattern Mining; Ontologies
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1 Introduction
The use of electronic support systems in health care is increasingly important (Hillestad et al. 2005).
Studies have shown that 90% of general practitioners (GPs) use a clinical software package, and 98%
of these GPs use the clinical packages for prescribing (McInnes, Saltman & Kidd 2006). This means
there is an abundance of rich heath data available on general practitioners computers. However, a
lot of this data is stored as free text, and as a result it is not easily interpreted. Using computers for
writing prescriptions offers several benefits for GP’s. Therefore prescription data is one of the most
complete and structured types of data in general practice (Hassey, Gerrett & Wilson 2001).
The prescription data can be represented in a number of ways to aid the GPs; such as tabular
summaries (Wroe et al. 2000), or as a series of connected nodes where the nodes represents
different prescriptions; forming a prescription pathway (Stanek et al. 2005). A prescription pathway
represents the sequence of prescriptions for a particular patient over a certain period of time as
prescribed by the GP. Creating pathways can make the data more interpretable, easier to follow, and
additional relationships between prescriptions may become apparent.
Figure 1: a prescription pathway
The concept of these pathways is based on the use of flat prescription data. However, drugs are by
nature hierarchical, as they belong to a series of categories. Hence they can be modelled in an
ontology; such as the Anatomical Therapeutic Chemical (ATC) drug classification, where different
levels of the ontology represent different groups of drugs (WHO 2010). The different levels range
from specific chemical substances to broad anatomical main groups (see figure 2). If an ontology like
this is used with the prescription drugs in the pathways, it is possible to analyse the prescription
pathway at different levels of granularity. For example, the pathway can be viewed at the most
specific level (which can be seen as using the original flat data), or the drugs can be abstracted into
chemical subgroups using the ATC drug classification, and then further in to pharmacological
subgroups, and so on.
Different information exists at different levels of the ontology, which can be useful for different
methods and applications. When observing a pathway, a low granularity implies a more general
pathway (for example, level 1 or 2 of the ATC drug classification) and might give the observer a
simpler understanding of what the pathway is trying to achieve. Meanwhile a high granularity gives a
more specific pathway (for example, ATC level 5), which gives a thorough understanding of the
specific drugs in the pathway.
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Figure 2: the ATC drug classification model and an example for Propicillin
Changing the granularity of the prescriptions can have precarious effects. For example, figure 2
shows a model for Propicillin. At level three it can be observed that it is part of the penicillins group.
Some people have allergies to Penicillins; so if data-mining for this pattern, logically the best level to
operate at is level 3. If the drugs are modelled at more specific levels (e.g. level 5 - J01CE03 Propicillin); the pattern gets weaker, as it is spread across a large amount of variables, hence the
pattern is diluted. It may get weaker to the extent that it does not emerge as a pattern at all,
resulting in a loss of information from the dataset. Conversely, if a lower granularity is used (e.g.
level 2 - J01 - Antibacterials for systemic use); the pattern showing penicillin allergy may disappear,
as the J02 group contains other groups of antibacterial drugs unrelated to the penicillin-type allergy.
In this sense, the pattern becomes contaminated by unrelated drugs. The concepts of dilution and
contamination are used throughout this paper as explained here.
These notions highlight the potential importance of incorporating an ontology knowledge base for
the discovery of patterns. The traditional approach to discovery of these pathways is sequential
pattern mining (SPM). Sequential pattern mining is the process of trying to find the relationships
between occurrences of sequential events, to find if there exists any specific order of the
occurrences (Zhao & Bhowmick 2003). By performing sequential pattern mining on the prescription
data and implementing the knowledge of a drug ontology, there is the potential to find many more
new and interesting patterns that are not present in the mining of flat data. There are several
algorithms to perform sequential pattern mining, which are explored in section 2.2.
2
1.1 Motivation
Traditional path mining algorithms work on flat data without taking into account hierarchies or
ontologies. This may not be sufficient in some cases. Prescription data is by nature hierarchical, so
there is an opportunity to explore the impact of an ontology on the sequential pattern mining in this
project. There is little current indication as to what effect a change in granularity has on the
usefulness of the sequential pattern mining. This research explores how changing the granularity
affects the sequence pattern discovery process. It investigates the concepts of dilution; that is, if the
exact chemical components used in each prescription are always mined; the associations may be too
weak, and contamination; if a low level of granularity is always selected, the pathways may be too
ambiguous. This research has wider implications in the field of data mining; as this use of an
ontology could benefit the approach to finding patterns in data, and obtaining more results from
datasets that can be modelled in an ontology.
For the health informatics domain; being able to ascertain which granularities are appropriate for
which purposes will make the prescription data far more useful for GPs. Simply omitting all other
granularities in favour of one results in the potential loss of important information. This research is
necessary to identify the effect of using an ontology on the pattern discovery for general practice
data.
1.2 Research Question
This project focuses on analysing the impact of applying an ontology for data mining ordinarily flat
data. The research question ‘what impact will the use of an ontology in place of flat data have on the
success of sequential pattern mining?’ is explored.
2 Literature Review
This section will focus on some previous research into several core aspects of this minor thesis.
Some similar work involving data mining in health informatics is explored, as well as some
supplementary literature to support aspects of the project; covering the data mining aspect with
some sequential pattern mining methods; the drug ontology with some conceptual work on
ontologies and some previous work with drug ontologies and granularity; and the nature of the raw
data with research regarding the use of electronic health records.
2.1 Data Mining in Health Informatics
There is a large amount of research in health informatics that uses data mining. Health informatics is
the science of applying Information Age technology to serve the specialised needs of public health
(Friede, Blum & McDonald 1995). Data mining has become a key benefactor to the progress of the
integration of health information systems into general practice.
This project draws on concepts proposed in the work by Stanek et al. (2005). Stanek et al. proposed
a method that compares practice patterns to clinical pathways. The research was focused on
patients with diabetes and hypertension. There was also use of the ATC drug coding system, but the
researchers did not fully utilise the hierarchical nature of the drug ontology for the data mining, and
instead used a set granularity for all experiments. Stanek et al.’s methods are tractable for smaller
domains; however the intent of this project is to apply a similar methodology to a far broader area,
where these methods quickly become problematic.
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Some other related work involved the adaption of Bayesian Networks for discovering temporal-state
transition patterns, specifically in the hemodialysis process (Lin, Chiu & Wu 2002). Lin, Chiu & Wu’s
research focused on learning clinical pathways, so that pathways for admitted patients could be
predicted. Lin, Chiu & Wu used a rich set of data including more attributes such as test results; and
created a set of states, events, and actions. The research proved very successful; but did not
implement knowledge of an ontology for the drugs. Also, Lin, Chiu & Wu’s research involved a
broader data set with attributes that are not available for this research. It was also specific to
hemodialysis, which limits its applications.
Bei et al. (2005) performed some correlated work with a system called Portal. Bei et al.’s research
was focused on improving the quality of procedures by giving continuous support to physicians. They
performed some rule extraction for the selection of pacemaker systems for new patients, and
implemented a simple business logic flowchart system to automatically classify new patients. A
flowchart type implementation is not be suitable for this project; due to the immense size of the
flowchart required to model all possible prescription pathways. The researchers went on to identify
the potential for data mining for long processes (such as long prescription patterns). Bei et al.’s
research highlighted the need for optimised support systems to reduce costs and improve the
quality of procedures.
Another associated piece of research was the use of the Hidden Markov Model (HMM) to learn
clinical pathways (Lin, Hsieh & Pan 2005). The researchers modelled the process of spontaneous
delivery of patients, and developed a 4-state pathway that accurately encompasses normal
spontaneous delivery. The model was trained with the patient data, and visualised in a manner that
simplifies the pathway for doctors. The intent was to learn the clinical pathway, so the outcomes
were defined; unlike the research proposed in this paper, which was poised to perform
unconstrained data mining the data for patterns and observe the results. There was also no use of a
drug ontology, and the data used was not strictly prescription data. The outcome of the research
was a model that accurately recreated clinical pathways that could be used to predict possible paths
for an admitted patient.
Work by Riou, Pouliquen & Beeux (1999) had the goal of predicting the best drug for a prescription
based on the clinical background of a patient. The methodology was not related to mining for
patterns, and was rather regarding analysing patient’s disorders, pathophysiological conditions, age,
and other factors to determine the next step in the clinical process. A tool was developed from the
premise that junior residents and medical students had difficulties selecting the most appropriate
drug for a given scenario. Riou, Pouliquen & Beeux proposed the use of the ATC codes, but decided
against it due to the limitations of only maintaining one use, and not fitting indicators or other
properties, and instead opted to develop their own drug knowledge base. Some issues in regard to
the use of the ATC drug classification for this project are explored in section 3.2.
Another piece of related research was regarding mining time dependency patterns in clinical
pathways (Lin et al. 2001). Lin et al. intended to find patterns of process execution sequences that
showcased the dependant relation between activities. The researchers developed a method to
discover the patterns of clinical pathways using patient records and clinical log data. The research
covered the broader domain of complete clinical pathways, so additional data was used in the
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process, whereas the research for this minor thesis focuses on the pathways relating to drug
prescriptions only. Furthermore, there is no use of ontologies, which is intended for this research.
2.2 Sequential Pattern Mining
The concept of mining for patterns in sequences of data has been implemented and improved in
many applications. It stems from the field of Data Mining; which is the process of extracting
interesting information or patterns from information repositories (Chen, Han & Yu 1996). Sequential
pattern mining (SPM) is the process of trying to find the relationships between occurrences of
sequential events, to find if there exists any specific order of the occurrences (Zhao & Bhowmick
2003). Many methods of SPM have been developed, some of which were explored and evaluated for
this project.
One of the earliest and possibly the simplest algorithms developed for SPM is AprioriAll (Agrawal &
Srikant 1995). It is based on the Apriori principle from data mining for association rules, and is a very
rudimentary method for finding sequential patterns. It finds single frequently occurring items in the
dataset and then attempts to find sequences of them. The straightforward nature of the algorithm
may make it serviceable for this project. However, there are some limitations; the algorithm cannot
detect frequent patterns within a sequence. For example, if one patient’s prescription history is used
as a sequence, patterns within that prescription history are not identified. The sequence may be
detected as a candidate, but if it is not frequent across all patients, it is not identified as a pattern. As
patters within each patient’s records are important, this is not sufficient.
PrefixSpan (Pei et al. 2004) is a more optimised alternative to AprioriAll. It provides the same
functionality at a lower computational cost. It works by recursively projecting the database into a set
of smaller databases and growing the patterns by recombining the sets. The authors found it to
address some of the shortcomings of the candidate generate-and-test methods, and in several
experiments it was shown to be the best performer. Although the algorithm is more efficient, it
possesses several undesired traits for this project. Similar to AprioriAll, it does not detect patterns
within sequences. Furthermore, the algorithm finds transitive patterns, i.e. patterns with gaps. For
example, for the sequence A -> B -> C; A -> B is as much a pattern as A -> C. This is detrimental to the
project’s outcomes, as each drug in a prescription pathway is very important, and occluding a drug
or series of drugs may produce misleading results.
SPIRIT (Sequential Pattern mining with Regular expression constraints) is an optimised algorithm
designed to mine user-specified patterns (Garofalakis, Rastogi & Shim 1999). This proves interesting
if searching for particular sequences of drug prescriptions, such as known clinical pathways. Dowsey
et al (1999) showed that the use of clinical pathways reduced the duration of admission for patients.
By searching for parts of clinical pathways, general practitioners adherence to them could be
evaluated. However, as this minor thesis is not directed at any particular set of drugs or clinical
pathways, this is not within the scope of this project.
There are also methods involving multiple attributes, called multi-dimensional sequential pattern
mining (Pinto et al. 2001). These are useful for adding new information such as age groups, dates, or
complex attributes to patterns. Due to the nature of the data, no other information is guaranteed to
be usable, so this was not implemented.
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Another extension on conventional pattern mining is incremental mining, introduced in
Parthasarathy et al.’s work (1999) and explored by Zhang et al. (2001). This is useful for datasets that
continue to change over time. In this project space the dataset is not changing so this has not been
implemented. This could prove as an interesting extension to the sequential pattern mining in the
future.
Periodic Pattern Analysis involves the limiting of pattern mining to certain periods, to specify when
to check for recurring patterns (Han, Dong & Yin 1999). Naturally this is most effective if the data is
collected over long periods of time. This could be used to explicitly find monthly or yearly patterns,
but does not regard the initial concern of testing the impact of ontologies on data mining.
Other research in the area includes optimising for linked objects in a distributed system (Chen, Park
& Yu 1998), and creating hybrid combinations of other methods (LeniC & Kokol 2002). Many
advanced and optimised methods have been developed for sequential pattern mining, but the
requirement of this minor thesis is a simple method for a proof-of-concept, so many prove
unsuitable.
2.3 Drug Ontologies
Ontologies are explicit formal specifications of the terms in the domain and relations among them
(Gruber 1993). Noy & McGuinness say ontologies are used to share common understanding of the
structure of information, to make domain assumptions explicit, to separate domain knowledge from
the operational knowledge and to analyse domain knowledge (2001). In an attempt to liken these
definitions to this project; the domain can be seen as the set of prescription drugs, along with a
formal definition of the explicit “is-a” relationship between ontology levels and their parent levels
(for example, each item at level 3 in the ontology is part of an item at level 2).
There are several implementations of ontologies for the domain of health informatics. As discussed
earlier, the Anatomical Therapeutic Chemical (ATC) drug classification is one such drug ontology
(WHO 2010). This ontology provides a simple hierarchical breakdown of the field of prescribable
drugs, and is easily accessible from the organisations website. Whilst the ATC classification does
have some flaws which are addressed in section 3.2, it is a widely used standard that provides a
malleable ontology for the development of a proof-of-concept system in a simple and practical
manner.
Rector et al. (1998) proposed some requirements for developing ontologies to be used in medicine.
Rector et al. identify that an ontology should be treated as an "assembly language", and that it
should be viewed as a “pure tree in which the branches at each level are disjoint but nonexhaustive
subconcepts of the parent concept” (Rector et al. 1998). These elements provide the basis of some
further work into developing drug ontologies.
One such piece of research involved the development of Prodigy: a reusable and automaticallyclassified ontology to describe the chemical composition of the drugs, as well as a dictionary of
prescribable products, which includes more volatile information such as the pack sizes and
preparations (Solomon et al. 1999). Whilst this does create a more robust and descriptive system, it
complicates the knowledge base by incorporating non-useful or unavailable data (in regard to this
research) into the drug ontology, so this method was not selected for this research.
6
Wroe and colleagues used a descriptive logic named Grail to implement an ontology based on
existing pathology and physiology ontologies to create formal descriptions of a generic drug’s clinical
properties. This was used to include indications, contradictions, side effects and other properties in
the definition of the drugs (Wroe et al. 2000). This proved promising for sorting and grouping drugs,
and possibly finding multi-dimensional patterns. However like with Solomon et al.’s research, this
served to complicate the knowledge base and made it intractable for this research.
One of the most influential pieces of work for this study was an investigation into the effects of data
granularity on data mining (Andrusiewicz & Orlowska 1997). Whilst granularity is not directly linked
to ontologies, they serve the same purpose as both involve abstracting data to more general cases
to enhance data mining. In Andrusiewicz & Orlowska’s work, a formal definition of some data mining
and database concepts is given, followed by an explanation of how the concept of changing data
granularity could influence the data mining outcomes. Andrusiewicz & Orlowska found that
decreasing the granularity (akin to moving up the ontology) can lead to the discovery of patterns
that were not present earlier. This observation is similar to the aforementioned definition of
dilution. The research demonstrated the effects of introducing data granularity to a data set, much
the same as this project’s introduction of an ontology.
2.4 Electronic Health Records
The data used for the program consists of electronic forms of patient records. Storing electronic
health records (EHR) has become prevalent in general practice. Keeping digital copies of health data
presents many opportunities as well as legal issues and complications, which are explored.
Hillestad et al. (2005) discussed the estimated savings, costs, safety benefits and other health
benefits, in order to show the potential profit that the use of electronic medical records can produce
for the industry. Hillestad et al. compared the use of I.T. in health to many other sectors such as
telecommunications, securities trading and retail to forecast the financial benefits of investing in
health informatics. Hillestad et al.‘s research showed the importance of dedicating resources to the
development of electronic health records, and showed the likelihood that the trend of increasing
adoption of EHR will continue, making it a valuable source of data.
There has been research involving extensions to EHR, such as the development of Virtual Medical
Records (vMR) (Johnson et al. 2001). These are an abstraction of conventional medical records;
stripped down to things necessary for modelling guidelines and protocols. This ongoing interest in
digital health information stresses how prevalent it is becoming, and further identifies it as a
valuable source of data.
Replicating health records on computer systems presents many legal issues regarding privacy and
ownership of information, as highlighted by Friedman (2006) and Hodge, Gostin & Jacobson (1999).
Debate continues to occur regarding the use of EHR for research, and this was the driving factor
behind the need to de-identify data before analysing it, to avoid any ethical and privacy issues.
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3 Methodology
In this minor thesis a proposal is given for a system to pre-process prescription data, construct
prescription pathways from the data, and then execute the sequential pattern mining on the
prescription information. A process flow that outlines the running of the program can be seen in
figure 3.
Figure 3: the program process flow
This section explains the nature of the raw data, the methods used to pre-process the data, as well
as an explanation of the sequential pattern mining method.
3.1 Raw data
3.1.1 Ethical Considerations
This research is based on real prescription data from general practices. The data used for this
research was used in similar previous studies and was properly de-identified (i.e. no identifiers or
other data related to the identity of the patient is contained in the data). In no way can this
information lead to re-identification of the persons by any members besides the data-providers.
Ethics approval was granted to re-use the dataset for the current study. The data collected will need
to be stored for seven years after the research is concluded, due to standard university procedures.
An ethics approval form has been submitted and approved to ensure to upmost adherence to ethical
research policies, which is included in Appendix A.
3.1.2 Data summary
The entire dataset contains 67,150 tuples for 2931 patients. A smaller dataset of 630 constituting 13
patients’ prescription histories has been extracted prior to this research, selected as being mostly
complete records and feasible for this proof-of-concept study.
Each tuple of the dataset contains the information for one single drug prescription. Cumulative
prescriptions for single patients are spread over multiple tuples. The data has already been stripped
of any identifying attributes, as addressed in section 3.1.1. Each tuple has the following attributes:
Attribute
patient_pkey
filenumber
provider_pkey
date
script_number
drug_name
dosage
dose
repeats
packsize
quantity
form
Meaning
A string of characters unique to each patient
Irrelevant
A string of characters unique to each prescription provider
The date the prescription was given
Irrelevant
The brand name of the drug prescribed in plain text
Notes on the dosage
How much of the drug to take at one time
How many times to repeat the prescription (0 = only one prescription)
How many dosages in a pack
Duplicate of packsize
The form of the prescribed drug (tablet, ointment, etc)
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formulary
druggen
generic
pbs
use
Irrelevant
Chemical name of the drug prescribed
Irrelevant
Irrelevant
The condition the prescribed drug is treating
Many of the attributes in the data are not required for this project. The patient_key is needed to link
prescriptions for the same patient. The druggen contains the chemical name of the drug, and is used
to get the ATC code for the prescription pathways. The date is used to ensure the prescription
pathway proceeds from the earliest point in time to the latest (i.e., it is a record of what drugs were
prescribed in what order). The dose, repeats and packsize can be combined to deduce how many
days the prescription lasts for. Together, this smaller set of attributes forms the dataset required for
this research.
3.2 Pre-processing
The data requires several stages of cleaning and processing before it is suitable for sequential path
mining. These stages include addressing missing values; recoding dosages; mapping drug names to
ATC codes; and constructing prescription pathways.
The first issue addressed is the possibility of missing values. The issue of missing values exists in any
dataset, despite the prescription data being the most reliable available (Hassey, Gerrett & Wilson
2001). Any tuples with missing prescription drugs are seen as a data entry error, and must be
removed. Whilst this is not ideal, it is a far better option than trying to fabricate a drug prescription
to overcome the data entry error. Missing values of packsize or dose are replaced with 1. Again, this
is not optimal, but the only negative effect of this is an inaccurate length of prescription duration.
Furthermore, this does not impact the data mining for this project, as this duration information is
not used. The repeats, date, and patient_key values are complete for all records.
The next step of the pre-processing is to recode the forms of the dose. Different GPs have the
tendency to record dosage information differently. Different methods of recording such as ‘1 daily’,
‘one’, ‘1 n’, etc need to be recoded to a uniform representation. This is performed by searching for
particular strings and modifying the dose number to reflect a dosage per day. For example, a base
dosagePerDay is initialised to 1. If the substring ‘per week’ is found, the dosagePerDay is divided by
7. If ‘3’ is found, the dosagePerDay is multiplied by 3, and so on. In this manner, doses such as ‘1/2 a
tablet 5 times per week’ are interpreted correctly, and exported as a standardised number of units
per day.
Figure 4: re-coding dosage information
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This information can then be combined with the packsize and repeats to calculate how many days a
prescription lasts for. However, as this research only regards the prescription pathways, and hence
has no notion of prescription durations, this is beyond the scope of this project. This aspect of the
software is provided as a foundation for further research.
The next step in the pre-processing is to replace the drug names with their respective ATC codes. A
Global is created in MUMPS (similar to an array in other programming languages) that maps all drug
names to their respective codes. This is generated by utilising the World Health Organisation
Collaborating Centre (WHOCC) online ATC classification index. The WHOCC offers a service where a
drug name can be input, and the respective ATC code is received. This is used to generate a Global of
ATC codes and drug names, which can be used to automatically map the drug names to ATC codes.
Figure 5: using the WHOCC online ATC index to obtain ATC codes
There are also several features of the ATC classification that are not desired for this minor thesis that
will now be addressed. Firstly, the ATC system assigns unique codes to certain combination drugs. In
this research, it is preferred if these combination drugs be represented by the codes for each of their
components – as if multiple single drugs were prescribed. This can be achieved by simply modifying
the Global to reflect these conversions. One other undesirable trait of the ATC drug classification in
relation to this project is the handlings of drugs that have multiple uses. Because the ATC drug
classification does not implement a multiple-hierarchy; if a drug has several uses or forms, it can
exist at multiple points in the ontology, and hence have multiple codes. As this is a large
implementation issue, and does not necessarily affect the path mining process, it is sufficient to omit
multiple codes for drugs in favour of a single code for this project.
The final step of the pre-processing is to generate a prescription pathway for each patient. The
prescription pathway is constructed by linking all sequential patient prescriptions, which are all the
tuples with the same patient_pkey. The prescription pathways are stored in a single multidimensional Global, where one dimension represents the patient number and the second dimension
represents the prescription number. This ensures enough information is maintained to determine
where patterns occur, i.e. in which patients and at what point in their prescription pathways.
3.3 Sequential Pattern Mining (SPM)
For the purpose of these studies, a modified sequential pattern mining algorithm has been created
to suit the data and requirements of the project. The flaws of the comparative algorithms are
discussed in relation to this project, as well as the benefits of the developed algorithm, followed by
an overview of the sequential pattern mining process.
10
3.3.1 Algorithm summary
The sequential pattern mining was performed based on the ATC drug codes. Due to the nature of
conventional SPM algorithms such as AprioriAll (Agrawal & Srikant 1995) and PrefixSpan (Pei et al.
2004), it would not be sufficient to simply feed in each patient’s pathways as a sequence, as this
would not yield meaningful results. This is because these algorithms simply test for the presence of
an itemset in a sequence, not for the frequency. For example, a sequence of Drug A -> Drug B ->
Drug A -> Drug B -> Drug A -> Drug B that is only given to one patient may be significant, but would
not be identified as a pattern.
For this reason, a modified sequential pattern mining algorithm is developed that finds patterns
within patients, as well as across the set of patients. This is achieved by feeding each part of the
pathway into the algorithm as a separate sequence, as shown below in figure 6. In this manner, any
and all frequently occurring patterns within patient’s pathways are discovered.
Figure 6: preparing the prescription pathways for the sequential pattern mining
This also addresses another issue of comparative SPM methods; transitive sequences. In a medical
context, the exact order of the prescriptions can be very important. However, the SPM algorithms
that were evaluated simply check for the presence of one item at some point after another item. For
example, if you consider the pathway in figure 6, “A01AA02 -> B01AB02” would be considered as
frequent as “A01AA02 -> C01CD02”, even though C01CD02 does not directly follow A01AA02. Due to
the length of the prescription pathways, many unrelated and misleading “patterns” may be
discovered by this property. The current SPM algorithms could be modified to prevent this from
occurring; however by isolating each sequential prescription as an individual sequence, these
problems are easily avoided.
To determine what is selected as a pattern from the frequently occurring sequences, two support
thresholds are used. A standard ‘minSupport’ is used; which is a minimum number of times the
sequence has to occur across all prescriptions. The second threshold is a ‘minPatient’; which is a
minimum number of patient prescription pathways the sequence has to occur in. The sequence is
considered to be a pattern if either of these criteria is met. Ignoring the first variable would result in
frequently occurring prescriptions within limited numbers of patients being missed. Ignoring the
second variable results in prescription patterns that are not re-prescribed to the same patients being
11
ignored (i.e. the pattern is prescribed to many patients, but since it does not re-occur, it does not
meet the minSupport). Using this dual-threshold method, the most frequent prescription sequences
across the whole dataset are discovered, as well as the prescriptions that occur in the most patients.
3.3.2 Sequential Pattern Mining process
To run the experiments, different levels of granularity of the underlying data are created (using the
ATC ontology to generate the tests). The level five “flat data” is analysed first (refer to figure 6). The
frequently occurring 2-item sequences are found by generating the candidate 2-item sequences and
counting how often each sequence occurs, similar to the AprioriAll method (Agrawal & Srikant
1995). Note that unlike AprioriAll, 1-item sequences are not generated, as they are not required. The
candidates are then tested against the two support thresholds, to obtain the patterns. This process
illustrated in figure 7.
Figure 7: generating patterns by testing candidates against support thresholds
From these 2-item patterns the 3-item candidates are generated by combining all frequent 2-item
patterns than occur sequentially in the same patients, and then testing against the support
thresholds again to deduce the patterns. This process continues until no candidates reach the
support thresholds, at which point execution is halted. This is illustrated in figure 8. This process
borrows from previous SPM methods, but is tuned to this project to obtain the most meaningful
results achievable.
Figure 8: combining 2-item patterns into 3-item candidates, and then halting because no 3-item patterns are discovered
12
Further experiments are then generated by recoding the pathways into higher levels of the ATC
ontology. For example; N05CD07 becomes N05CD, A10BA02 becomes A10BA, etc. After searching
for patterns at this level, drugs are recoded further, for example N05CD becomes N05C. This process
iterates until patterns mining at all five levels of the ATC drug ontology has been completed.
4 Results
The above methodology was implemented on the dataset containing 630 prescriptions across 13
patients. The program generated patterns and output them to file for manual interpretation. After
some experimenting, a minSupport of 6 and minPatient of 3 were selected for delivering optimal
results. This is equivalent is approximately 1% of all prescriptions and 25% of patients. In other
words, for a sequence to be identified as a pattern, it must occur in 1% of the total number of
prescriptions, or in 25% of the patients.
4.1 Output Format
Some example output is given in figure 9, and the entire set of results can be found in Appendix B.
The figure below gives a view of how the results are delivered to the user. The drug names and
codes are displayed, as well as the index of each occurrence (giving the patient and the prescription
number for that patient). The total number of occurrences (which must be more than minSupport)
and the number of patients (which must be more than minPatients) are also displayed. This
formatting provides all the required information in a neat and readable manner, which is required
for manually inspecting patterns.
Figure 9: example output of pattern mining program
The program also provides summaries after each stage of the data mining, as seen in figure 10. This
is beneficial for obtaining statistics about the overall progress and success of the pattern mining.
Figure 10: a summary for a stage of pattern mining
13
4.2 Flat Data Pattern Mining Results
Using the support thresholds of minSupport = 6 and minPatients = 3 with the original ATC level 5
data, the sequential pattern mining generated a total of 55 2-item patterns, of which there were 8
unique patterns. No 3-item sequences met either of the support thresholds, so no 3-item patterns
were identified. These results represent a baseline as the limited set of outcomes produced from
standard analysis of flat data. The patterns found are displayed in figure 11. Note how the first two
patterns only meet the minPatient threshold, while the 3rd-5th patterns only meet the minSupport. If
only one of these thresholds was used, many of these patterns would be missed.
Figure 11: a summary of the patterns discovered for the flat data
4.3 Ontology Data Pattern Mining Results
More patterns started to emerge as the codes were modified and abstracted further up the
ontology. Patterns consisting of more than two prescriptions also began to appear. The full set of
results is attached in Appendix B, and the results are summarised in the below table.
Level
5
4
3
3
2
2
1
1
1
1
1
Pattern Length
2
2
2
3
2
3
2
3
4
5
6
Unique Patterns
8
11
15
1
20
1
31
25
11
2
1
14
Total Patterns
55
73
109
6
152
6
430
211
73
17
7
The “level” indicates the stage of the ATC drug classification, for example level 5 is the chemical
substances, e.g. J01CE03, and level 1 is the anatomical main groups, e.g. J (see figure 2 for a
summary of each level). The “pattern length” indicates how many items were in the pattern, for
example a 2-length pattern may be J01 -> B01, meanwhile a 3-length pattern may be J01 -> B01 ->
C03.
Number of Patterns
The graph in figure 12 compares the number of 2-length patterns than are discovered at different
levels of the ATC ontology. This gives an insight as to how the success of the pattern mining is
impacted by the change in the granularity.
500
450
400
350
300
250
200
150
100
50
0
TotalPatterns
UniquePatterns
5
4
3
2
1
Ontology Level
Figure 12: a comparison of number of 2-length patterns found at each level of ATC ontology
Number of Patterns
From these results, an increase in the amount of patterns occurring as the prescription data moves
further up the ontology is observed. Of particular mention is the drastic increase as the data moves
to level 1 pattern mining; 430 total patterns are found. This number is especially large when you
consider there were only 630 total prescriptions in the dataset; almost 70% of the 2-item sequences
are now frequent. The level 1 patterns also reach up to 6 items in length, far longer than the
previous levels. The level 1 results are visualised below.
500
450
400
350
300
250
200
150
100
50
0
TotalPatterns
UniquePatterns
2
3
4
5
6
Number of items in pattern
Figure 13: the ATC level 1 patterns
15
This graph illustrates the large difference between the total number of patterns found and the
number of unique patterns. This is especially apparent when pattern mining at this higher level of
the ontology. There is also a significant decline in patterns as the number of items in the patterns
increases.
5 Discussion
In this section, the results are interpreted, providing some evidence of the existence of
contamination and dilution in granular data. The impact of using ontologies in place of flat data will
then be discussed, followed by the interestingness of patterns discovered from using ontologies.
5.1 Results Analysis
The results show a large increase in emergent patterns (in particular, total patterns) as the
prescriptions progress up the ATC ontology. This is because as the data progresses up the ontology,
each item below is contained by the item above. This means that each pattern at level n will also be
a pattern at level n-1, as the items at level n are contained in the level n-1 representation (along with
a group of other drugs). Furthermore, as the prescriptions move up the ontology and the number of
possible states (and hence the number of possible combinations of these states) decreases,
mathematically more patterns emerge.
Whilst there may be an increase in the number of patterns, this does not necessarily result in a gain
in information from the data. The concepts of contamination and dilution are explored below, to
investigate the effects of using an ontology on the results of the pattern mining. After manually
inspecting the results, three cases are presented for analysis. The first is an example of dilution:
Figure 14: an example of dilution
This pattern was found at level 4 of the ontology mining, and represents the equally most frequent
pattern at this level. As such it can be seen as one of the most important patterns. However, the
pattern does not occur lower in the ontology at level 5; as at that point the added information
spreads the pattern over multiple sequences, which do not meet the minimum support (in this case;
C09AA05 -> C10AA05, which occurs 4 times in 1 patient, and C09AA10 -> C10AA05, which occurs 5
times in 1 patient). This is an example of dilution; performing the sequential pattern mining on the
more specific data results in this potentially meaningful pattern being missed.
The second case is an example of contamination. Figure 15 shows a selection of results from the
output that demonstrates this concept:
Figure 15: an example of contamination
16
The figure illustrates one pattern as it traverses up the ontology. This can be seen by the similarity of
the codes (A10BA02 -> A10BA -> A10B). At level 5, the sequence A10BA02 -> A10BB09 is already a
frequently occurring pattern. As mining is performed at higher levels of the ontology, additional
information is compounded into the pattern. For example, at level 4, the pattern now includes ‘all
Biguanides’ instead of just Metformin, even though the pattern is actually only true for Metformin.
By level 3, the pattern has collapsed to a single state (A10B -> A10B), which is essentially not a
pattern at all, and rather a re-prescription. As such, the pattern has been made meaningless by the
addition of unrelated groups of drugs to the pattern. The reoccurring pattern of A10B is not as
meaningful as A10BA02 -> A10BB09; and the pattern has been contaminated.
The third and final example is an unusual pattern:
Figure 16: an unusual pattern found from pattern mining
While this pattern is present at lower levels, it is far easier to identify and interpret at this more
abstract level, especially for medical non-professionals. As a pattern that involves hypnotics and
sedatives, it is potentially unusual and may indicate a problematic patient, or interesting prescription
methodology. This use of the ontology to represent information at a more abstract level could be
used by practitioners to more easily identify and address unusual prescription patterns. This ease of
identification is another case for the use of ontologies like the ATC drug classification to further data
mining, as it makes the analysis of emergent patterns more understandable.
These example patterns give an idea of the kind of additional information that it available when an
ontology is incorporated into an ordinarily flat database. Discovery of new patterns, variance of
pattern interpretation and conceptual abstraction are all expected to appear in any dataset this is
implemented with. For the case of prescription information, a large difference between the mining
of flat data and the mining of hierarchical data that is implemented in an ontology is observed.
5.2 Using ontologies
The results of this work have shown promise in addressing the research question; regarding what
impact the use on an ontology in place of flat data has on the success of sequential pattern mining.
This research has demonstrated that the ability to detect patterns in the dataset depends on the
selection of the correct granularity. As the granularity is decreased, more patterns emerge, but due
to the decline in specificity of the pattern, they may be less meaningful. There has been some similar
research into the effects of granularity on pattern mining (Andrusiewicz & Orlowska 1997).
Andrusiewicz & Orlowska’s proposed using abstractions of original data to strengthen the patterns
found by data mining. This is the same principle as modelling the data in an ontology, as the
abstraction of information serves to minimise the set of possible states and hence the combination
of these states. The researchers had similar findings; that decreasing granularity could lead to the
discovery of additional patterns, but it did not necessarily make them meaningful or interesting. For
example, in Andrusiewicz & Orlowska’s work it is observed that the pattern of “people who buy
bread also buy milk” may be quite interesting; however the abstraction of the pattern to “people
who buy bread also buy food” makes it meaningless. This is explored further in section 5.3.
17
The introduction of granularity to the dataset also realises the concepts of contamination and
dilution, as demonstrated in section 5.1.These phenomena are expected not to be isolated to this
project and the use of prescription data, and rather will be present in other datasets. Dilution can
occur whether the dataset is hierarchical or not, as it occurs when the granularity of the data is too
high for important patterns to emerge. This is especially prevalent in datasets containing attributes
with vast ranges of values. The more values there are available; the more combinations of these
values there are possible; and mathematically less likelihood combinations will be frequent enough
to be patterns. Often the original data in a dataset is that of a very high, specific granularity, and
hence many patterns may be missed due to the vast number of values. Adding levels of abstraction
to the data helps to identify patterns that may not have been apparent at more precise levels of
granularity.
Contamination is a concept that is introduced by the use of ontologies, and is not present in ordinary
flat data. As the data abstracts further up the ontology, and the concepts get more generic, there is
a risk of patterns becoming too ambiguous and meaningless. If a strong pattern emerges, and the
data is abstracted further, this contaminates it with unrelated concepts, and hence the pattern
becomes less clear. This experience is particularly important in prescription data, but may have
implications in other datasets.
5.3 Interestingness
As mentioned earlier, the use of ontologies does increase the discovery of patterns, but it does not
necessarily mean the patterns are interesting. Ascertaining just what constitutes an interesting
pattern is difficult. Ideally, a user study would be conducted with industry professionals to try to
build a model for determining the interestingness of a pattern. However, due to time limitations,
this is not possible in this project. Hence, interestingness is defined broadly for this project as a
pattern’s potential usefulness and meaningfulness to the industry professionals for whom the data
relates to (in this case, general practitioners).
As the data abstracts further up the ontology and the granularity decreases, the patterns become
more conceptually generic. To some degree, this means a loss in meaning, as the specificities of the
pattern are not contained in the abstracted sequence. For example, if a pattern is discovered at level
3 of the ATC drug classification involving J01C – Penicillins, this may be considered interesting.
However, the exact penicillins that were involved is not known at this level, so there may be little
opportunity to act on this pattern. It is apparent there is some trade-off between the additional
discovery of potentially interesting patterns and the loss of specific information in those patterns.
Andrusiewicz & Orlowska (1997) explore this notion, and argue that adding levels of granularity can
increase interestingness to a point, after which the patterns become too general. Andrusiewicz &
Orlowska’s argument extends to this project, where using an ontology does lead to the discovery of
additional patterns which may be interesting. However, in this project, simply abstracting patterns
does not make them more interesting in any case; it merely makes them easier to analyse and
interpret. This may be isolated to the field of prescription data, as the degree of detail is required for
the patterns to be interesting to general practitioners.
18
5.4 Further implications
This project used a limited set of data and operated in a very limited time frame. With more data
and further refinement of methods, this research could ultimately show how much detail is required
for prescription information to be meaningful and usable, and how much abstraction of the
pathways is possible before the patterns become too ambiguous to be significant. This could have
potential applications to GPs analysing prescription information in practice. Better delivery of
prescription pathways to GPs could help identify anomaly cases; identify practitioner’s trends in
prescribing; and monitor adherence to clinical pathways. With practitioner support, a developed
system could be integrated in the practice review process for GPs to reflect on and adapt methods.
6 Future Work
There are several ways in which further work could be conducted for this project. These include the
possibility to implement therapeutic pathways in place of prescription pathways; changing
granularity dynamically; as well as the potential for further work in regard to interestingness.
Additionally, this project may form the basis of further research into different fields to test the
effects of ontologies on other domains and datasets.
6.1 Therapeutic pathways
One possible limitation of this project is the omission of time related information. If several drugs
are prescribed at the same time (for example, drugs B1 and B2), they are stored in the database as
separate prescriptions, but have the same ‘date’ attribute. A prescription pathway does not account
for the date of the prescription and to this effect, interprets this as B1 followed by B2. There has
been some previous research involving development of therapeutic state-transition graphs
(Gadzhanova et al. 2007), which will be referred to as therapeutic pathways. The therapeutic
pathway is a combination of the drug prescriptions and the amounts prescribed; to give an idea of
what combination of drugs was being taken at any given time.
Figure 17: a prescription pathway and its equivalent therapeutic pathway
By utilising this approach, the discovered patterns might be more representative of the true
implications of the data, rather than just the manner in which the data is stored. Whilst this minor
thesis provides an insight into the general effects of an ontology on data mining, for the results to be
more meaningful, therapeutic pathways may be implemented. This would prevent multi-drug
19
prescriptions from emerging as patters in the dataset, and so would be a welcome modification to
make the software more applicable to general practice.
6.2 Changing Granularity dynamically
In both Andrusiewicz & Orlowska’s work (1997) and in this project, the granularity (or level of
ontology) is changed statically for all items at the same time (for example, in this project, all
prescriptions are moved from one level of the ATC drug classification to the next level at the same
time). There is an opportunity to explore the effects of dynamically changing the level of individual
items independent to one another. Using this technique could lead to the discovery of patterns that
fluctuate between levels of the ATC ontology, such as A10BA02 -> C10 -> B05BB. Searching for
patterns in this manner could help discover what types of data (in a prescription data context)
require what level of granularity to produce meaningful patterns.
6.3 Exploring Interestingness
Although this project has discussed interestingness to some extent, there is still much work that be
conducted regarding this term. As mentioned, there is no formal definition in regard to what
constitutes an interesting pattern. Logically, a beneficial pattern is one that provides a gain in
information. User studies with general practitioners could be used to deduce just what granularities
of different types of prescription data provide useful information, which could be used to develop a
better definition for the interestingness of a pattern. A set of heuristics could possibly be used to
assess the interestingness of a pattern.
With a tentative definition for interestingness, research into whether changing granularity results in
a change to interestingness could be conducted. Furthermore, this extended knowledge of
interestingness could be used to pre-process related data. Rather than mapping a flat database into
a complete ontology as was done in this project, the data could be mapped into the most
appropriate or ‘interesting’ levels of the ontology initially, and then the data mining could be
performed. This would mean that less candidate patterns would be generated and the algorithm
would generally run more efficiently.
6.4 Applying to other fields
There is also an opportunity to apply this research to other fields. Currently this research is limited to
the area of prescription data for health informatics. There are many domains where data is naturally
hierarchical, such as store purchase data and chemicals interactions, and could be modelled in an
ontology. By mapping other datasets to ontology based databases, the effects of added granularity
could be further explored, to ensure the results presented here are not isolated to prescription data.
Due to the reasoning for the increase in pattern discovery (i.e. reducing the number of states and
hence the possible combinations of these states), it is unlikely that other fields would have
drastically differing results. However, research could also be conducted with regard to different
types of ontologies with different dependencies between nodes, such as “is-a-part-of” or “is-acause-of”, instead of the single “is-a” dependency of this project.
20
7 Conclusion
In this project the effects of using an ontology in place of flat data for the purpose of performing
sequential pattern mining on patient prescription data has been explored. A sequential pattern
mining method has been developed that can search for patterns in drug prescriptions modelled at
different levels of the ATC drug classification. The algorithm finds patterns within patients as well as
across all patients, and uses two support thresholds to produce the most meaningful and interesting
patterns.
The experiments have shown that there is an increase in pattern discovery at less granular levels of
the ontology; however it is difficult to ascertain if these patterns are more interesting. There is also
evidence of dilution; where using a low level of the ontology results in important patterns being
omitted, and contamination; where using a high level of the ontology results in important patterns
becoming ambiguous or meaningless.
This research has provided a proof-of-concept into the effects of ontologies on sequential pattern
mining. It has demonstrated the potential importance of implementing an ontology in place of flat
data to further data mining efforts, and has shown the need for further research into this topic. This
work has provided evidence that the use of ontologies can lead to the discovery of additional
patterns, and also addressed some of the issues that come from the use of ontologies in data
mining.
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9 Project Timeline
Date
5th March 2010
12th March 2010
th
13 March – 15th March 2010
16th March - 31st March 2010
1st April – 14th April 2010
15th April 2010
16th April – 16th May 2010
17th May – 1st June 2010
3rd June – 8th June
11th June 2010
13th June 2010
14th June - 25th July 2010
26th July – 1st August 2010
2nd August – 22nd August 2010
23rd August – 30th September 2010
10th October 2010
24th October 2010
22nd November 2010
29th November 2010
Task
Chose Supervisor
Decide on field of thesis
Develop Project Plan
Research topic
Write annotated bibliography
Submit Annotated Bibliography
Finalise Research Question
Write Minor Thesis introduction, literature review
Work on ethics proposal for obtaining data
Write Minor Thesis ethical considerations
Prepare presentation slides
Write Minor Thesis methodology
Finalise presentation slides
Minor Thesis Proposal presentation
Submit Minor Thesis Proposal
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Familiarising with Cache programming suite
Program Pre-processing, pathway output
Program testing
Implementing Sequential Pattern Mining methods
Evaluating Sequential Pattern Mining results
Write Minor Thesis results and discussion
Minor Thesis draft to supervisor
Submit Minor Thesis
Comments for corrections received, adjust Minor Thesis
Submit Final bound copies
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Appendix A – Ethics Approval Application
University of South Australia
Human Ethics Application
Protocol Number : 0000020574
Application Title : Impact of applying Hierarchical Structure to typically Flat Data for path mining prescription pathways
Date of Submission : N/A
Primary Investigator : Mr Matheson Lee Ramsey
Prior Assessment
Non-UniSA HREC
UniSA HREC
Project details
Research Ethics Advisor
Project type
1.1 Has another Human Research Ethics Committee (other than UniSA) reviewed this research project before and does
this clearance/approval accurately describe the project as it is to be conducted?*
Yes No
2.1 Is this application a resubmission of an application that was considered by UniSA HREC and the decision was 'Not
Approved: Resubmit', 'Not Approved' or "Approved subject to" and the status has expired (ie amendments were not
made within the 6 month timeframe.
Please note if your application is Approved subject to and 6 months has not lapsed then you should use the application
you submitted to make the required changes. *
Yes: Not approved: resubmit
Yes: Not Approved
Yes: Approved subject to and the status has expired
No
3.1 Name of Research Ethics Adviser
This question is not answered.
3.2 Has the Research Ethics Adviser conducted an ethics workshop in the last 12 months?*
No
3.3 Have you attended human ethics training in the last 12 months?*
Yes No
4.1 Main type of research (e.g. staff, PhD). *
Honours
Course Approval
PhD
Masters by Course work
Masters by Research
Professional Doctorate
Undergraduate
Graduate Diploma / Graduate Certificate
Staff
Other
4.2.1 Please note that, if you are a student applicant, your application will be forwarded to your principal supervisor
once submitted for their approval. If they are satisfied with your application it will be forwarded to the relevant review
group. If your supervisor requires changes to be made then your application will be returned to you to make the
required changes.
4.3 Other type of research (e.g. staff, PhD). Please select all that apply*
None
Honours
Course Approval
PhD
Masters by Course work
Masters by Research
Professional Doctorate
Undergraduate
Graduate Diploma / Graduate Certificate
Staff
Other
25
Project details
Resources
Project funding
Ownership of Data
5.1 Title of research project*
Impact of applying Hierarchical Structure to typically Flat Data for path mining prescription pathways
5.2 Plain English title*
Testing what kind of impact on path mining success of applying a hierarchical structure to data that is
normally not stored in a hierarchy, such as prescription data
5.3 What are the aims of your research*
-evaluate the impact and usefulness of using a hierarchy to store typically flat data
-evaluate what levels in the hierarchy produce the strongest and most useful paths
5.4 List your research questions or hypotheses. Your protocol should clearly identify the questions which you want your
research to answer.*
What is the impact on path mining of applying a hierarchy structure to typically flat data?
5.5 Explain the need for, and value of, your research. Place the aims in the context of existing research or practice.
(You must include a list of not more than 10 key references as an attachment to support your answer to this question.
These are to be attached to the Attachment tab available from the Application Overview screen).*
The research presents an opportunity to explore and understand how vague information can be before it is no
longer useful (the higher up a hierarchy the more vague the information is, as it encompasses more
elements).
It also presents an opportunity to discover the impact of using a hierarchy to elaborate typically flat data. If a
mark improvement is found, this could lead to the adoption of hierarchical structures for other applications,
which could lead to increased running speeds or more accurate path mining, depending on the domain.
There is a need to explore the impact of using a hierarchy as some flat data (such as prescription drug
information) is too vast to perform complete path mining in feasible time frames.
5.6 Proposed commencement date*
05/07/2010
5.7 Proposed completion date*
01/11/2010
6.1 Have you applied for funding for this project (other than divisional funds)*
Yes No
8.1 Detail who will own the data and the results of your research (student researchers normally own their own research
and data unless there is a written agreement between the student and the University / third party; staff research and
data is normally owned by UniSA).
Please select all that apply.*
UniSA
Student researcher
Other
8.2 Does the owner of the information or any other party have any right to impose limitations or conditions on the
publication of the results
of this project?*
Yes No
8.3 Please note that it is the researcher's responsibility to ensure that, where required, an appropriate agreement is in
place. If you are unsure whether this is needed, please consult the UniSA website . Do you require an agreement
regarding ownership or do you currently have an agreement in place?*
An agreement is required A signed agreement is in place An agreement is not required
Please note that you must inform UniSA HREC once the agreement has been signed. Final ethics approval cannot be
given until confirmation is received.
9.1 The information which will be stored at the completion of this project is of the following type(s). Please select all
that apply.*
Individually identifiable
Re-identifiable
Non-identifiable
9.2 Where will the data be stored (please be specific with the address e.g. If stored at UniSA please specify which
campus and the office/room location)*
the data will be stored at the Mawson Lakes campus of UniSA in D2-03
9.3 For how long will the information be stored after the completion of the project? Why has this period been chosen?*
5 years - to ensure any queries after the completion of the project can be answered, and to quell any later
accusations of copying.
9.4 In what formats will the information be stored during the research project? (eg. paper copy, computer file on floppy
disk or CD, audio tape, USB memory stick, videotape, film). *
computer file
9.5 How will information, in all forms, be disposed after the retention time has lapsed? (Please refer to the Ownership
and Retention of Data Policy. The Head of School (or equivalent) must be aware of this process.*
deletion of computer file and any backups (on the single same machine)
26
9.6 Will any other individual(s), organisation(s) or researcher(s) (other than those listed on the Investigators tab) have
authority to use or have access to the information? *
Yes No
9.7 Specify the measures to be taken to ensure the security of information from misuse, loss, or unauthorised access
while stored during the research project? (eg. will identifiers be removed and at what stage? Will the information be
physically stored in a locked cabinet?)*
the data will be immediately de-identified as this identifying information is of no use to the study. The data
will be stored on a laptop computer using a strong user password to protect from misuse.
9.8 What arrangements are in place with regard to the storage of the information collected for, used in, or generated
by this project in the event that the principal researcher / investigator ceases to be engaged at the current
organisation? (Please refer to the Ownership and Retention of Data Policy.*
If the principal researcher ceases to be engaged in the study the data will become the responsibility of the
supervisor Jan Stanek
10.1 Please refer to the UniSA website : Do you require insurance cover for this project"*
Yes No
11.1 Is the activity archival research? A large proportion of activity involving the analysis of documents, publicly
available information, or previously collected data may be outside the scope of the University's human research ethics
arrangements.*
Yes No
11.2 Is the work being conducted only for UniSA administrative / service delivery purposes?*
Yes No
Scope
Scope
Research type and participants
Research type
Participant information
12.1 Should the work be characterised as quality assurance or an audit, rather than human research within the scope of
the University's human research ethics arrangements?*
Yes No
12.2 Is the work a practical exercise or test conducted for teaching purposes in a University administered facility? (
Please refer to Appendix 2 of Guidelines for Evaluation Activities Involving UniSA Students and Staff) *
Yes No
13.1 Is the work a routine experiment or procedure conducted for teaching purposes in a University administered
facility? *
Yes No
13.2 Is the work / data collection conducted by a student only for teaching / learning purposes? *
Yes No
13.2.1 Will the results be published / presented in any way other than a paper / product produced purely for
assessment purposes ?*
Yes No
14.1 This project involves: (Please select all that apply.)*
Research using qualitative methods
Research using quantitative methods, population level data or databanks, e.g survey research, epidemiological research
None of the above
14.2 What research methodologies will you use? (Please select all that apply.) *
Anonymous questionnaires
Internet questionnaires
Questionnaires requesting intimate personal, identifying, or sensitive information
Other questionnaires
Face to face interviews which do not request personal or sensitive information
Face to face interviews which request personal or sensitive information
Telephone survey which does not request personal or sensitive information
Telephone survey which requests personal or sensitive information
Focus groups
Action Research
Observation of participant's usual activities
Observation of an activity set up for the purposes of the study
Access to medical records (or records which contain intimate personal information, and are individually identifiable and
are not publicly available)
Experiment or testing of a procedure, drug or equipment
Use of biological hazards, GMOs or pathogenic organisms
Use of carcinogenic and/or toxic chemicals, including heavy metals
Use of Radiation (Ionising and/or Non-ionising, but not Ultrasound)
Other
14.2.1 Please describe what research methodology you will use.*
none of these methodologies apply. We are only interested in obtaining the de-identified data.
27
14.3 Will you be audio-taping, video-taping, or taking photographs of participants during the course of the study?
Please select all that apply.*
Audio-taping
Videotaping
Photographs
No
Selection of participants
Project start, end, location details
Irregular consent process
Limited disclosure / waive consent
Covert observations
15.1 How many participant groups are involved in this research project? *
0
15.3 What is the expected total number of participants in this project at all sites?*
0
16.1 What process(es) will be used to identify potential participants?*
there are no participants
16.2 Will potential participants be 'screened' or given a test/questionnaire to assess their suitability as a participant for
the study?*
Yes No
16.3 Describe how initial contact will be made with potential participants.*
No contact
16.4 Is an advertisement, e-mail, website, letter or telephone call proposed as the form of initial contact with potential
participants?*
Yes No
16.5 List the selection and, if appropriate to your study, the exclusion criteria for participants.*
there are no participants
16.6 If it became known that a person or participant group was recruited to, participated in, or was excluded from the
research, would that knowledge expose the person to any disadvantage or risk?*
Yes No Not Applicable
17.1 Will the research be undertaken in Australia?*
Yes No
17.1.1 In which town(s)/city(ies)/State(s) of Australia will the research be undertaken in? *
Adelaide, South Australia
17.1.2 In how many Australian organisations will the research be conducted? *
0
17.2 Will the research be undertaken overseas?*
Yes No
17.3 Are there any time-critical aspects of the research project of which the review committee should be aware?*
Yes No
18.1 Does the research involve limited disclosure to participants. Please refer to the National Statement. *
Yes No
18.2 Are you asking the HREC / review body to waive the requirement of consent? Refer to the National Statement*
Yes No
19.1 Does the research involve covert observation? Refer to the National Statement*
Yes No
Deception
Project type
Project type
Participants
Recruitment
Risk to Participants
Risk to participants
Right to Privacy
20.1 Does the research involve deception. Refer to the National Statement*
Yes No
21.1 Does the research involve any of the following? Please select all that apply.*
Drugs, narcotics, poisons, placebo will be ingested / injected, or an invasive procedure will be administered
Clinical trials
Cellular therapy
The collection and / or use of human samples. This includes tissue, blood or other body fluid collection / extraction
Genetic testing and/or genetic research
Human gametes or use or creation of human embryos
A practice or intervention which is an alternative to a standard practice or intervention
Investigating workplace practices which could possibly impact on workplace relationships
Conducting the research overseas and recruiting participants
None of the above
28
38.1 Who will you be recruiting as participants for this study? (If there is a high chance that you will be recruiting one
of these groups, you
should also select that participant group).*
General public (over 18 years of age)
Members of a collectivity
People whose first language is not English
People who are illiterate
Pregnant women/human foetus
Children
People who are in a dependent or unequal relationship
People who are highly dependent on medical care
People with a cognitive impairment
Aboriginal and/or Torres Strait Islander peoples
People who may be involved in illegal activity
Not recruiting participants
Other
38.2 Does the research involve issues likely to be considered significant to Indigenous peoples?*
Yes No Not Applicable
51.1 Please select all that apply. This research project:*
Has the potential to expose participants to potential civil, criminal or other proceedings
Makes it possible for third parties to identify participants
Involves a risk of physical injury
Involves human exposure to ionising and/or non-ionising radiation (including X-ray)
Involves exposure to disease or infection
Involves pain or significant discomfort
Involves psychological or emotional stress
Involves sensitive personal information
Could expose participants to potential loss of professional reputation, market standing, or employability
Could result in significant negative impact upon personal relations
Offers an inducement which could be considered coercive
Involves the participation of people who legally cannot provide voluntary and informed consent for their participation in
research
None of the above
Collection method
Collection method
Participants Relationships
Consent
Consent process
66.1 Does IS42 or the Commonwealth Privacy Act apply to the research (eg access to identified personal data held by
third parties subject to privacy regimes)? Refer to the Privacy law*
Yes No
67.1 Will the source of the information about participants used in this research project be collected directly from the
participant? (e.g. asking participants directly about their medical history)*
Yes No
67.2 Will the source of the information about participants used in this research project be collected from another person
about the participant? (e.g. asking participants' doctors about their patients medical history)*
Yes No
67.3 Will the source of the information about participants involve the use or disclosure of information by an agency,
authority or organization (other than UniSA)? (e.g. accessing participants' medical records)*
Yes No
67.4 Will the source of the information about participants involve the use of information which you or your organisation
Collected previously for a purpose other than this research project?*
Yes No
67.5 Describe how information collected about participants will be used in this project.*
data will be de-identified immediately as the identifying information poses no use for the study.
67.6 Indicate whichever of the following applies to this project: Please select all that apply.*
Information collected for, used in, or generated by, this project will not be used for any other purpose.
Information collected for, used in, or generated by, this project will/may be used for another purpose by
the researcher for which ethical approval will be sought.
Information collected for, used in, or generated by, this project is intended to be used for establishing a database/data
collection/register for future use by the researcher for which ethical approval will be sought.
Information collected for, used in, or generated by, this project will/may be made available to a third party for a
subsequent use or which ethical approval will be sought
Other
68.1 Is there an existing relationship or one likely to arise during the research, between the potential participants and
any member of the research team or an organisation involved in the research?*
Yes No
29
68.2 Does the researcher / investigator have another role in relation to the participant?*
Yes No
68.3 Will the research impact upon, or change, an existing relationship between participants and researcher /
investigator or organisations.?*
Yes No
69.1 Will consent for participation in this research be sought from all participants? Refer to the National Statement*
Yes No
69.1.1 Explain why consent will not be sought from all participants.*
there are no participants.
70.1 Describe the consent process, ie how participants or those deciding for them will be informed about, and choose
whether or not to
participate in, the project.*
no participants
Risks and benefits
Risks and benefits
Risks and benefits cont.
Researcher training
70.2 If a participant or person on behalf of a participant chooses not to participate, are there specific consequences of
which they should be made aware, prior to making this decision?*
Yes No
70.3 If a participant or person on behalf of a participant chooses to withdraw from the research, are there specific
consequences of which they should be made aware, prior to giving consent?*
Yes No
70.4 Can individual participants be identifiable by other members of their group? (e.g. co-workers, focus group
members etc.)*
Yes No
70.7 Will consent be specific or extended or unspecified? Refer to section 2.2.14-2.2.18 of the National Statement*
Specific Extended Unspecific
Please note that when answering the following questions, only risks beyond those encountered in
everyday life are relevant. Refer to the National Statement
71.1 Are there any risks to participants as a result of participation in this research project (eg physical, psychological,
spiritual, emotional,
legal, social, financial well-being, employability or professional relationships)?*
Yes No
71.2 What expected benefits (if any) will this research have for the wider community?*
-provide an insight into the requirement for specificity of useful informational - IE, in a hierarchical structure,
how far up the hierarchy can we go before paths that are mined are too vague?
-allow for future work which can autonomously detect the most useful level of information for a specific
purpose. This could result in more accurate prescriptions, and more successful treatments.
71.3 What expected benefits (if any) will this research have for participants?*
data being de-identified means there will be no personal benefit
71.4 Are there any other risks involved in this research? eg. to the research team, the organisation, others (eg physical,
psychological, spiritual, emotional, legal, social, financial well-being, employability or professional relationships)*
Yes No
72.1 Is it anticipated that the research will lead to commercial benefit for the investigator(s) and or the research
sponsor(s)?*
Yes No
72.2 Is there a risk that the dissemination of results could cause harm of any kind to individual participants - whether
their physical, psychological, spiritual, emotional, legal, social or financial well-being, or to their employability or
professional relationships - or to their communities?*
Yes No
72.3 Describe how the researchers / investigators intend to monitor the conduct and progress of the research project?*
-data will be de-identified at first opportunity. There will be no opportunities for misconduct as long as the
data is successfully de-identified.
72.4 It is mandatory for researchers to report suspected cases of child abuse/neglect, domestic violence,
bullying, illegal activities, use of illicit substances, abuse of elderly persons, professional negligence etc.
72.4.1 Is it likely that this will be disclosed during the course? *
Yes No
73.1 List the relevant qualifications, experiences and /or skills of the research team which equip them to conduct this
research*
3 years study at UniSA learning ethical conduct
30
Reporting of results
Reporting of results cont.
Peer review
Declaration
Minor experience in health informatics with research placement and ongoing work with minor thesis
73.2 Do the researchers involved in this research project require any additional training in order to undertake this
research?*
Yes No
74.1 Is it intended that results of the research that relate to a specific participant be reported to that participant?*
Yes No Not Applicable
74.2 Is the research likely to produce information of personal significance to individual participants?*
Yes No
74.3 Will individual participant's results be recorded with their personal records?*
Yes No Not Applicable
74.4 Is it intended that all or some of the results that relate to a specific participant be reported to anyone other than
that participant?*
Yes No
74.5 Will research participants have the opportunity to receive a copy of your final report or summary of the findings if
they wish?*
Yes No
74.5.2 Why will participants not be provided with a copy of the final report or summary of the findings?*
there are no participants
75.1 Is the research likely to reveal a significant risk to the health or well being of persons other than the participant
(eg family members, colleagues)?*
Yes No
75.2 Is there a risk that the dissemination of results could cause harm of any kind to individual participants - whether
their physical, psychological, spiritual, emotional, social or financial well-being, or to their employability or professional
relationships - or to their communities?*
Yes No
75.3 How is it intended to disseminate the results of the research? Please select all that apply.*
Thesis/dissertation
Journal article/s
Research paper
Conference presentation
Commissioned report
Other
75.4 Will the confidentiality of participants and their data be protected in the dissemination of research results?*
Yes No Not Applicable
75.4.1 Explain how confidentiality of participants and their data will be protected in the dissemination of research
results*
de identified data will be used, and so no confidential information will be revealed in the dissemination
76.1 Provide details of the anticipated duration of the data collection / human research phase of the project.*
simple obtain some data from previous researcher and/or database - collection should take no longer than 1-2
days
76.2 Has the research proposal, including design, methodology and evaluation undergone, or will it undergo, a peer
review process?*
Yes No
Declaration
The Primary Contact for this project is responsible for the application that is submitted and must be the
one to agree to
the following statement.
"On behalf of the research team for this project, I confirm that all members of the research have read the current
NHMRC National Statement on Ethical Conduct in Human Research. The research team accepts responsibility for the
ethical and appropriate conduct of the procedures detailed in this application, confirm that the research team will
conduct this project in accordance with the principles described in the National Statement, and confirm that the
research team will comply with any other condition laid down by the University of South Australia's Human Research
Ethics Committee."*
I agree
31
Appendix B – Results
LEVEL 5 : 2 ITEM PATTERNS
PATTERN: Metformin (A10BA02) -> Metformin (A10BA02)
PATTERN OCCURS 5 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 63) (4, 16) (13, 9) (13, 15) (13, 30)
PATTERN: Metformin (A10BA02) -> Gliclazide (A10BB09)
PATTERN OCCURS 5 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 64) (4, 1) (4, 3) (13, 45) (13, 49)
PATTERN: Gliclazide (A10BB09) -> Metformin (A10BA02)
PATTERN OCCURS 7 TIMES IN TOTAL OF 2 PATIENTS
OCCURENCES: (4, 2) (4, 4) (4, 9) (4, 12) (4, 22) (13, 25) (13, 34)
PATTERN: Prazosin (C02CA01) -> Verapamil (C08DA01)
PATTERN OCCURS 6 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (11, 7) (11, 48) (11, 54) (11, 66) (11, 79) (11, 89)
PATTERN: Verapamil (C08DA01) -> Metformin (A10BA02)
PATTERN OCCURS 9 TIMES IN TOTAL OF 2 PATIENTS
OCCURENCES: (4, 39) (4, 41) (11, 13) (11, 19) (11, 24) (11, 36) (11, 42) (11, 55) (11, 67)
PATTERN: Atorvastatin (C10AA05) -> Atorvastatin (C10AA05)
PATTERN OCCURS 6 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (5, 18) (10, 8) (10, 12) (10, 20) (10, 47) (11, 51)
PATTERN: Paracetamol (N02BE01) -> Paracetamol (N02BE01)
PATTERN OCCURS 9 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (3, 20) (3, 38) (3, 41) (3, 42) (3, 43) (3, 53) (3, 59) (3, 68) (3, 96)
PATTERN: Temazepam (N05CD07) -> Paracetamol (N02BE01)
PATTERN OCCURS 8 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (3, 49) (3, 52) (3, 56) (3, 58) (3, 65) (3, 67) (3, 80) (3, 100)
32
--total of 55 patterns found.
total of 8 unique patterns.
the largest single pattern occurred 9 times.
---
LEVEL 4 : 2 ITEM PATTERNS
PATTERN: Biguanides (A10BA) -> Biguanides (A10BA)
PATTERN OCCURS 5 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 63) (4, 16) (13, 9) (13, 15) (13, 30)
PATTERN: Biguanides (A10BA) -> Sulfonamides, urea derivatives (A10BB)
PATTERN OCCURS 5 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 64) (4, 1) (4, 3) (13, 45) (13, 49)
PATTERN: Sulfonamides, urea derivatives (A10BB) -> Biguanides (A10BA)
PATTERN OCCURS 7 TIMES IN TOTAL OF 2 PATIENTS
OCCURENCES: (4, 2) (4, 4) (4, 9) (4, 12) (4, 22) (13, 25) (13, 34)
PATTERN: Alpha-adrenoreceptor antagonists (C02CA) -> Phenylalkylamine derivatives (C08DA)
PATTERN OCCURS 6 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (11, 7) (11, 48) (11, 54) (11, 66) (11, 79) (11, 89)
PATTERN: Sulfonamides, plain (C03BA) -> ACE inhibitors, plain (C09AA)
PATTERN OCCURS 4 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 28) (9, 9) (9, 17) (10, 41)
PATTERN: Phenylalkylamine derivatives (C08DA) -> Biguanides (A10BA)
PATTERN OCCURS 9 TIMES IN TOTAL OF 2 PATIENTS
OCCURENCES: (4, 39) (4, 41) (11, 13) (11, 19) (11, 24) (11, 36) (11, 42) (11, 55) (11, 67)
33
PATTERN: ACE inhibitors, plain (C09AA) -> ACE inhibitors, plain (C09AA)
PATTERN OCCURS 5 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (4, 6) (4, 7) (9, 4) (10, 2) (10, 22)
PATTERN: ACE inhibitors, plain (C09AA) -> HMG CoA reductase inhibitors (C10AA)
PATTERN OCCURS 9 TIMES IN TOTAL OF 2 PATIENTS
OCCURENCES: (9, 5) (9, 7) (9, 10) (9, 18) (10, 7) (10, 11) (10, 19) (10, 23) (10, 29)
PATTERN: HMG CoA reductase inhibitors (C10AA) -> HMG CoA reductase inhibitors (C10AA)
PATTERN OCCURS 6 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (5, 18) (10, 8) (10, 12) (10, 20) (10, 47) (11, 51)
PATTERN: Anilides (N02BE) -> Anilides (N02BE)
PATTERN OCCURS 9 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (3, 20) (3, 38) (3, 41) (3, 42) (3, 43) (3, 53) (3, 59) (3, 68) (3, 96)
PATTERN: Benzodiazepine derivatives (N05CD) -> Anilides (N02BE)
PATTERN OCCURS 8 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (3, 49) (3, 52) (3, 56) (3, 58) (3, 65) (3, 67) (3, 80) (3, 100)
--total of 73 patterns found.
total of 11 unique patterns.
the largest single pattern occurred 9 times.
---
LEVEL 3 : 2 ITEM PATTERNS
PATTERN: ORAL BLOOD GLUCOSE LOWERING DRUGS (A10B) -> ORAL BLOOD GLUCOSE LOWERING
DRUGS (A10B)
PATTERN OCCURS 19 TIMES IN TOTAL OF 3 PATIENTS
34
OCCURENCES: (1, 63) (1, 64) (4, 1) (4, 2) (4, 3) (4, 4) (4, 9) (4, 12) (4, 15) (4, 16) (4, 17) (4, 22)
(13, 9) (13, 15) (13, 25) (13, 30) (13, 34) (13, 45) (13, 49)
PATTERN: ORAL BLOOD GLUCOSE LOWERING DRUGS (A10B) -> SELECTIVE CALCIUM CHANNEL
BLOCKERS WITH MAINLY VASCULAR EFFECTS (C08C)
PATTERN OCCURS 6 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (13, 1) (13, 23) (13, 26) (13, 35) (13, 42) (13, 46)
PATTERN: ORAL BLOOD GLUCOSE LOWERING DRUGS (A10B) -> ACE INHIBITORS, PLAIN (C09A)
PATTERN OCCURS 6 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (4, 5) (4, 10) (4, 13) (4, 23) (4, 29) (4, 31)
PATTERN: ANTIADRENERGIC AGENTS, PERIPHERALLY ACTING (C02C) -> SELECTIVE CALCIUM
CHANNEL BLOCKERS WITH DIRECT CARDIAC EFFECTS (C08D)
PATTERN OCCURS 6 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (11, 7) (11, 48) (11, 54) (11, 66) (11, 79) (11, 89)
PATTERN: LOW-CEILING DIURETICS, EXCL. THIAZIDES (C03B) -> ACE INHIBITORS, PLAIN (C09A)
PATTERN OCCURS 4 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 28) (9, 9) (9, 17) (10, 41)
PATTERN: SELECTIVE CALCIUM CHANNEL BLOCKERS WITH DIRECT CARDIAC EFFECTS (C08D) -> ORAL
BLOOD GLUCOSE LOWERING DRUGS (A10B)
PATTERN OCCURS 9 TIMES IN TOTAL OF 2 PATIENTS
OCCURENCES: (4, 39) (4, 41) (11, 13) (11, 19) (11, 24) (11, 36) (11, 42) (11, 55) (11, 67)
PATTERN: ACE INHIBITORS, PLAIN (C09A) -> ACE INHIBITORS, PLAIN (C09A)
PATTERN OCCURS 5 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (4, 6) (4, 7) (9, 4) (10, 2) (10, 22)
PATTERN: ACE INHIBITORS, PLAIN (C09A) -> CHOLESTEROL AND TRIGLYCERIDE REDUCERS (C10A)
PATTERN OCCURS 9 TIMES IN TOTAL OF 2 PATIENTS
OCCURENCES: (9, 5) (9, 7) (9, 10) (9, 18) (10, 7) (10, 11) (10, 19) (10, 23) (10, 29)
PATTERN: CHOLESTEROL AND TRIGLYCERIDE REDUCERS (C10A) -> CHOLESTEROL AND TRIGLYCERIDE
REDUCERS (C10A)
PATTERN OCCURS 6 TIMES IN TOTAL OF 3 PATIENTS
35
OCCURENCES: (5, 18) (10, 8) (10, 12) (10, 20) (10, 47) (11, 51)
PATTERN: CHOLESTEROL AND TRIGLYCERIDE REDUCERS (C10A) -> ANTIGOUT PREPARATIONS (M04A)
PATTERN OCCURS 6 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (10, 9) (10, 13) (10, 24) (10, 30) (10, 39) (10, 48)
PATTERN: VIRAL VACCINES (J07B) -> ORAL BLOOD GLUCOSE LOWERING DRUGS (A10B)
PATTERN OCCURS 5 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 67) (4, 21) (4, 28) (4, 35) (13, 29)
PATTERN: OTHER ANALGESICS AND ANTIPYRETICS (N02B) -> OTHER ANALGESICS AND ANTIPYRETICS
(N02B)
PATTERN OCCURS 10 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (3, 20) (3, 38) (3, 41) (3, 42) (3, 43) (3, 47) (3, 53) (3, 59) (3, 68) (3, 96)
PATTERN: OTHER ANALGESICS AND ANTIPYRETICS (N02B) -> HYPNOTICS AND SEDATIVES (N05C)
PATTERN OCCURS 6 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (3, 48) (3, 50) (3, 57) (3, 66) (3, 78) (3, 89)
PATTERN: HYPNOTICS AND SEDATIVES (N05C) -> OTHER ANALGESICS AND ANTIPYRETICS (N02B)
PATTERN OCCURS 9 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (3, 49) (3, 52) (3, 56) (3, 58) (3, 65) (3, 67) (3, 80) (3, 90) (3, 100)
PATTERN: OTHER DRUGS FOR OBSTRUCTIVE AIRWAY DISEASES, INHALANTS (R03B) -> ADRENERGICS,
INHALANTS (R03A)
PATTERN OCCURS 3 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (7, 15) (10, 45) (11, 59)
--total of 109 patterns found.
total of 15 unique patterns.
the largest single pattern occurred 19 times.
---
36
LEVEL 3 : 3 ITEM PATTERNS
PATTERN: ORAL BLOOD GLUCOSE LOWERING DRUGS (A10B) -> ORAL BLOOD GLUCOSE LOWERING
DRUGS (A10B) -> ORAL BLOOD GLUCOSE LOWERING DRUGS (A10B)
PATTERN OCCURS 6 TIMES IN TOTAL OF 2 PATIENTS
OCCURENCES: (1, 63) (4, 1) (4, 2) (4, 3) (4, 15) (4, 16)
--total of 6 patterns found.
total of 1 unique patterns.
the largest single pattern occurred 6 times.
---
LEVEL 2 : 2 ITEM PATTERNS
PATTERN: DRUGS USED IN DIABETES (A10) -> DRUGS USED IN DIABETES (A10)
PATTERN OCCURS 19 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 63) (1, 64) (4, 1) (4, 2) (4, 3) (4, 4) (4, 9) (4, 12) (4, 15) (4, 16) (4, 17) (4, 22)
(13, 9) (13, 15) (13, 25) (13, 30) (13, 34) (13, 45) (13, 49)
PATTERN: DRUGS USED IN DIABETES (A10) -> CALCIUM CHANNEL BLOCKERS (C08)
PATTERN OCCURS 7 TIMES IN TOTAL OF 2 PATIENTS
OCCURENCES: (4, 40) (13, 1) (13, 23) (13, 26) (13, 35) (13, 42) (13, 46)
PATTERN: DRUGS USED IN DIABETES (A10) -> AGENTS ACTING ON THE RENIN-ANGIOTENSIN SYSTEM
(C09)
PATTERN OCCURS 7 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (4, 5) (4, 10) (4, 13) (4, 23) (4, 29) (4, 31) (4, 42)
PATTERN: DRUGS USED IN DIABETES (A10) -> VACCINES (J07)
PATTERN OCCURS 6 TIMES IN TOTAL OF 4 PATIENTS
OCCURENCES: (4, 27) (4, 34) (8, 8) (9, 20) (13, 12) (13, 38)
37
PATTERN: ANTIHYPERTENSIVES (C02) -> CALCIUM CHANNEL BLOCKERS (C08)
PATTERN OCCURS 10 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (11, 7) (11, 15) (11, 21) (11, 26) (11, 34) (11, 48) (11, 54) (11, 66) (11, 79) (11,
89)
PATTERN: DIURETICS (C03) -> AGENTS ACTING ON THE RENIN-ANGIOTENSIN SYSTEM (C09)
PATTERN OCCURS 4 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 28) (9, 9) (9, 17) (10, 41)
PATTERN: CALCIUM CHANNEL BLOCKERS (C08) -> DRUGS USED IN DIABETES (A10)
PATTERN OCCURS 11 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (4, 39) (4, 41) (11, 13) (11, 19) (11, 24) (11, 36) (11, 42) (11, 55) (11, 67) (13,
24) (13, 33)
PATTERN: CALCIUM CHANNEL BLOCKERS (C08) -> CALCIUM CHANNEL BLOCKERS (C08)
PATTERN OCCURS 6 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (3, 2) (5, 1) (5, 2) (5, 5) (5, 12) (11, 35)
PATTERN: AGENTS ACTING ON THE RENIN-ANGIOTENSIN SYSTEM (C09) -> DRUGS USED IN DIABETES
(A10)
PATTERN OCCURS 6 TIMES IN TOTAL OF 2 PATIENTS
OCCURENCES: (1, 49) (4, 8) (4, 11) (4, 14) (4, 26) (4, 30)
PATTERN: AGENTS ACTING ON THE RENIN-ANGIOTENSIN SYSTEM (C09) -> AGENTS ACTING ON THE
RENIN-ANGIOTENSIN SYSTEM (C09)
PATTERN OCCURS 5 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (4, 6) (4, 7) (9, 4) (10, 2) (10, 22)
PATTERN: AGENTS ACTING ON THE RENIN-ANGIOTENSIN SYSTEM (C09) -> SERUM LIPID REDUCING
AGENTS (C10)
PATTERN OCCURS 9 TIMES IN TOTAL OF 2 PATIENTS
OCCURENCES: (9, 5) (9, 7) (9, 10) (9, 18) (10, 7) (10, 11) (10, 19) (10, 23) (10, 29)
PATTERN: SERUM LIPID REDUCING AGENTS (C10) -> SERUM LIPID REDUCING AGENTS (C10)
PATTERN OCCURS 6 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (5, 18) (10, 8) (10, 12) (10, 20) (10, 47) (11, 51)
38
PATTERN: SERUM LIPID REDUCING AGENTS (C10) -> ANTIGOUT PREPARATIONS (M04)
PATTERN OCCURS 6 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (10, 9) (10, 13) (10, 24) (10, 30) (10, 39) (10, 48)
PATTERN: ANTIBACTERIALS FOR SYSTEMIC USE (J01) -> BETA BLOCKING AGENTS (C07)
PATTERN OCCURS 3 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 61) (3, 28) (6, 46)
PATTERN: ANTIBACTERIALS FOR SYSTEMIC USE (J01) -> ANALGESICS (N02)
PATTERN OCCURS 5 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (3, 9) (3, 40) (5, 7) (11, 4) (11, 46)
PATTERN: VACCINES (J07) -> DRUGS USED IN DIABETES (A10)
PATTERN OCCURS 6 TIMES IN TOTAL OF 4 PATIENTS
OCCURENCES: (1, 67) (4, 21) (4, 28) (4, 35) (8, 9) (13, 29)
PATTERN: ANALGESICS (N02) -> ANALGESICS (N02)
PATTERN OCCURS 11 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (3, 20) (3, 38) (3, 41) (3, 42) (3, 43) (3, 47) (3, 53) (3, 59) (3, 68) (3, 69) (3, 96)
PATTERN: ANALGESICS (N02) -> PSYCHOLEPTICS (N05)
PATTERN OCCURS 8 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (3, 4) (3, 13) (3, 48) (3, 50) (3, 57) (3, 66) (3, 78) (3, 89)
PATTERN: PSYCHOLEPTICS (N05) -> ANALGESICS (N02)
PATTERN OCCURS 10 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (3, 49) (3, 52) (3, 56) (3, 58) (3, 65) (3, 67) (3, 80) (3, 88) (3, 90) (3, 100)
PATTERN: DRUGS FOR OBSTRUCTIVE AIRWAY DISEASES (R03) -> DRUGS FOR OBSTRUCTIVE AIRWAY
DISEASES (R03)
PATTERN OCCURS 7 TIMES IN TOTAL OF 4 PATIENTS
OCCURENCES: (6, 21) (7, 7) (7, 15) (7, 16) (10, 44) (10, 45) (11, 59)
--total of 152 patterns found.
39
total of 20 unique patterns.
the largest single pattern occurred 19 times.
---
LEVEL 2 : 3 ITEM PATTERNS
PATTERN: DRUGS USED IN DIABETES (A10) -> DRUGS USED IN DIABETES (A10) -> DRUGS USED IN
DIABETES (A10)
PATTERN OCCURS 6 TIMES IN TOTAL OF 2 PATIENTS
OCCURENCES: (1, 63) (4, 1) (4, 2) (4, 3) (4, 15) (4, 16)
--total of 6 patterns found.
total of 1 unique patterns.
the largest single pattern occurred 6 times.
---
LEVEL 1 : 2 ITEM PATTERNS
PATTERN: ALIMENTARY TRACT AND METABOLISM (A) -> ALIMENTARY TRACT AND METABOLISM (A)
PATTERN OCCURS 22 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 39) (1, 56) (1, 59) (1, 63) (1, 64) (4, 1) (4, 2) (4, 3) (4, 4) (4, 9) (4, 12) (4, 15)
(4, 16) (4, 17) (4, 22) (13, 9) (13, 15) (13, 25) (13, 30) (13, 34) (13, 45) (13, 49)
PATTERN: ALIMENTARY TRACT AND METABOLISM (A) -> CARDIOVASCULAR SYSTEM (C)
PATTERN OCCURS 33 TIMES IN TOTAL OF 6 PATIENTS
OCCURENCES: (1, 18) (1, 26) (1, 34) (1, 40) (1, 57) (1, 68) (3, 82) (3, 86) (4, 5) (4, 10) (4, 13)
(4, 23) (4, 29) (4, 31) (4, 40) (4, 42) (11, 6) (11, 14) (11, 20) (11, 25) (11, 77) (11, 87) (12, 6) (12, 45)
(13, 1) (13, 10) (13, 16) (13, 19) (13, 23) (13, 26) (13, 35) (13, 42) (13, 46)
PATTERN: ALIMENTARY TRACT AND METABOLISM (A) -> C;C (C;C)
40
PATTERN OCCURS 4 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (4, 18) (8, 10) (13, 21) (13, 31)
PATTERN: ALIMENTARY TRACT AND METABOLISM (A) -> SYSTEMIC HORMONAL PREPARATIONS,
EXCL. SEX HORMONES AND INSULINS (H)
PATTERN OCCURS 4 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 8) (11, 68) (11, 72) (12, 10)
PATTERN: ALIMENTARY TRACT AND METABOLISM (A) -> ANTIINFECTIVES FOR SYSTEMIC USE (J)
PATTERN OCCURS 12 TIMES IN TOTAL OF 7 PATIENTS
OCCURENCES: (1, 42) (1, 52) (1, 60) (4, 27) (4, 34) (4, 36) (8, 8) (9, 20) (11, 3) (12, 3) (13, 12)
(13, 38)
PATTERN: ALIMENTARY TRACT AND METABOLISM (A) -> NERVOUS SYSTEM (N)
PATTERN OCCURS 8 TIMES IN TOTAL OF 2 PATIENTS
OCCURENCES: (1, 11) (1, 22) (1, 50) (3, 71) (3, 74) (3, 77) (3, 84) (3, 99)
PATTERN: CARDIOVASCULAR SYSTEM (C) -> ALIMENTARY TRACT AND METABOLISM (A)
PATTERN OCCURS 34 TIMES IN TOTAL OF 8 PATIENTS
OCCURENCES: (1, 19) (1, 41) (1, 49) (1, 58) (1, 62) (3, 73) (3, 83) (3, 98) (4, 8) (4, 11) (4, 14)
(4, 26) (4, 30) (4, 39) (4, 41) (7, 13) (9, 19) (11, 2) (11, 13) (11, 19) (11, 24) (11, 36) (11, 42) (11, 55)
(11, 67) (12, 9) (12, 46) (13, 8) (13, 11) (13, 14) (13, 18) (13, 20) (13, 24) (13, 33)
PATTERN: CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR SYSTEM (C)
PATTERN OCCURS 70 TIMES IN TOTAL OF 9 PATIENTS
OCCURENCES: (1, 13) (1, 27) (1, 28) (3, 2) (3, 22) (3, 25) (3, 31) (3, 32) (3, 33) (3, 34) (3, 35)
(3, 36) (3, 45) (3, 94) (4, 6) (4, 7) (5, 1) (5, 2) (5, 5) (5, 12) (5, 17) (5, 18) (7, 9) (9, 3) (9, 4) (9, 5) (9, 6)
(9, 7) (9, 8) (9, 9) (9, 10) (9, 14) (9, 17) (9, 18) (10, 2) (10, 7) (10, 8) (10, 11) (10, 12) (10, 19) (10, 20)
(10, 21) (10, 22) (10, 23) (10, 28) (10, 29) (10, 37) (10, 38) (10, 41) (10, 47) (11, 1) (11, 7) (11, 8) (11,
15) (11, 21) (11, 22) (11, 23) (11, 26) (11, 34) (11, 35) (11, 48) (11, 51) (11, 54) (11, 66) (11, 78) (11,
79) (11, 88) (11, 89) (13, 7) (13, 17)
PATTERN: CARDIOVASCULAR SYSTEM (C) -> C;C (C;C)
PATTERN OCCURS 8 TIMES IN TOTAL OF 2 PATIENTS
OCCURENCES: (11, 49) (11, 52) (11, 64) (13, 27) (13, 36) (13, 40) (13, 43) (13, 47)
PATTERN: CARDIOVASCULAR SYSTEM (C) -> SYSTEMIC HORMONAL PREPARATIONS, EXCL. SEX
HORMONES AND INSULINS (H)
41
PATTERN OCCURS 5 TIMES IN TOTAL OF 4 PATIENTS
OCCURENCES: (1, 14) (3, 61) (5, 13) (5, 19) (11, 90)
PATTERN: CARDIOVASCULAR SYSTEM (C) -> ANTIINFECTIVES FOR SYSTEMIC USE (J)
PATTERN OCCURS 12 TIMES IN TOTAL OF 5 PATIENTS
OCCURENCES: (4, 24) (4, 32) (4, 43) (5, 3) (5, 6) (5, 9) (5, 15) (6, 2) (6, 5) (6, 47) (9, 1) (12, 37)
PATTERN: CARDIOVASCULAR SYSTEM (C) -> MUSCULO-SKELETAL SYSTEM (M)
PATTERN OCCURS 15 TIMES IN TOTAL OF 4 PATIENTS
OCCURENCES: (7, 4) (10, 5) (10, 9) (10, 13) (10, 17) (10, 24) (10, 30) (10, 35) (10, 39) (10, 42)
(10, 48) (11, 27) (11, 32) (11, 80) (12, 15)
PATTERN: CARDIOVASCULAR SYSTEM (C) -> NERVOUS SYSTEM (N)
PATTERN OCCURS 19 TIMES IN TOTAL OF 4 PATIENTS
OCCURENCES: (1, 29) (3, 3) (3, 6) (3, 12) (3, 15) (3, 17) (3, 19) (3, 23) (3, 26) (3, 29) (3, 37) (3,
46) (3, 64) (3, 87) (3, 92) (3, 95) (8, 2) (11, 9) (11, 16)
PATTERN: DERMATOLOGICALS (D) -> CARDIOVASCULAR SYSTEM (C)
PATTERN OCCURS 3 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (3, 1) (10, 16) (12, 42)
PATTERN: GENITO URINARY SYSTEM AND SEX HORMONES (G) -> CARDIOVASCULAR SYSTEM (C)
PATTERN OCCURS 4 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (9, 16) (12, 8) (12, 14) (13, 6)
PATTERN: G;G (G;G) -> ALIMENTARY TRACT AND METABOLISM (A)
PATTERN OCCURS 6 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (1, 10) (1, 17) (1, 21) (1, 25) (1, 33) (1, 55)
PATTERN: SYSTEMIC HORMONAL PREPARATIONS, EXCL. SEX HORMONES AND INSULINS (H) ->
ANTIINFECTIVES FOR SYSTEMIC USE (J)
PATTERN OCCURS 4 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (6, 8) (11, 69) (11, 73) (12, 24)
PATTERN: SYSTEMIC HORMONAL PREPARATIONS, EXCL. SEX HORMONES AND INSULINS (H) ->
NERVOUS SYSTEM (N)
PATTERN OCCURS 9 TIMES IN TOTAL OF 3 PATIENTS
42
OCCURENCES: (3, 55) (3, 62) (6, 10) (6, 17) (6, 23) (6, 28) (6, 43) (12, 11) (12, 35)
PATTERN: ANTIINFECTIVES FOR SYSTEMIC USE (J) -> ALIMENTARY TRACT AND METABOLISM (A)
PATTERN OCCURS 10 TIMES IN TOTAL OF 6 PATIENTS
OCCURENCES: (1, 67) (3, 76) (4, 21) (4, 28) (4, 33) (4, 35) (4, 44) (8, 9) (12, 27) (13, 29)
PATTERN: ANTIINFECTIVES FOR SYSTEMIC USE (J) -> CARDIOVASCULAR SYSTEM (C)
PATTERN OCCURS 13 TIMES IN TOTAL OF 7 PATIENTS
OCCURENCES: (1, 3) (1, 61) (3, 11) (3, 28) (4, 25) (4, 38) (5, 4) (5, 16) (6, 46) (9, 2) (9, 21) (13,
13) (13, 39)
PATTERN: ANTIINFECTIVES FOR SYSTEMIC USE (J) -> SYSTEMIC HORMONAL PREPARATIONS, EXCL.
SEX HORMONES AND INSULINS (H)
PATTERN OCCURS 7 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (5, 10) (6, 3) (6, 7) (6, 9) (11, 29) (11, 70) (11, 74)
PATTERN: ANTIINFECTIVES FOR SYSTEMIC USE (J) -> ANTIINFECTIVES FOR SYSTEMIC USE (J)
PATTERN OCCURS 7 TIMES IN TOTAL OF 4 PATIENTS
OCCURENCES: (1, 31) (1, 53) (4, 20) (4, 37) (6, 6) (12, 25) (12, 26)
PATTERN: ANTIINFECTIVES FOR SYSTEMIC USE (J) -> NERVOUS SYSTEM (N)
PATTERN OCCURS 10 TIMES IN TOTAL OF 5 PATIENTS
OCCURENCES: (1, 43) (3, 9) (3, 40) (5, 7) (6, 14) (6, 19) (6, 33) (11, 4) (11, 46) (11, 83)
PATTERN: MUSCULO-SKELETAL SYSTEM (M) -> CARDIOVASCULAR SYSTEM (C)
PATTERN OCCURS 15 TIMES IN TOTAL OF 4 PATIENTS
OCCURENCES: (7, 5) (7, 12) (10, 1) (10, 6) (10, 10) (10, 18) (10, 27) (10, 34) (10, 36) (10, 40)
(10, 49) (11, 12) (11, 33) (12, 16) (12, 29)
PATTERN: MUSCULO-SKELETAL SYSTEM (M) -> MUSCULO-SKELETAL SYSTEM (M)
PATTERN OCCURS 10 TIMES IN TOTAL OF 4 PATIENTS
OCCURENCES: (1, 6) (2, 1) (2, 2) (10, 14) (10, 25) (10, 26) (10, 31) (10, 32) (10, 33) (11, 11)
PATTERN: NERVOUS SYSTEM (N) -> ALIMENTARY TRACT AND METABOLISM (A)
PATTERN OCCURS 7 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 38) (1, 44) (1, 51) (3, 70) (3, 81) (3, 85) (11, 5)
PATTERN: NERVOUS SYSTEM (N) -> CARDIOVASCULAR SYSTEM (C)
43
PATTERN OCCURS 22 TIMES IN TOTAL OF 7 PATIENTS
OCCURENCES: (1, 12) (1, 23) (3, 5) (3, 14) (3, 16) (3, 18) (3, 21) (3, 24) (3, 30) (3, 44) (3, 60)
(3, 63) (3, 72) (3, 91) (3, 93) (3, 97) (5, 8) (6, 1) (8, 1) (8, 3) (11, 47) (12, 36)
PATTERN: NERVOUS SYSTEM (N) -> ANTIINFECTIVES FOR SYSTEMIC USE (J)
PATTERN OCCURS 11 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 30) (3, 10) (3, 27) (3, 39) (3, 75) (6, 13) (6, 18) (6, 29) (6, 32) (6, 39) (6, 45)
PATTERN: NERVOUS SYSTEM (N) -> NERVOUS SYSTEM (N)
PATTERN OCCURS 34 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (3, 4) (3, 13) (3, 20) (3, 38) (3, 41) (3, 42) (3, 43) (3, 47) (3, 48) (3, 49) (3, 50)
(3, 51) (3, 52) (3, 53) (3, 56) (3, 57) (3, 58) (3, 59) (3, 65) (3, 66) (3, 67) (3, 68) (3, 69) (3, 78) (3, 79) (3,
80) (3, 88) (3, 89) (3, 90) (3, 96) (3, 100) (6, 24) (6, 44) (12, 12)
PATTERN: RESPIRATORY SYSTEM (R) -> CARDIOVASCULAR SYSTEM (C)
PATTERN OCCURS 5 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (7, 1) (7, 3) (7, 8) (10, 46) (11, 41)
PATTERN: RESPIRATORY SYSTEM (R) -> RESPIRATORY SYSTEM (R)
PATTERN OCCURS 7 TIMES IN TOTAL OF 4 PATIENTS
OCCURENCES: (6, 21) (7, 7) (7, 15) (7, 16) (10, 44) (10, 45) (11, 59)
--total of 430 patterns found.
total of 31 unique patterns.
the largest single pattern occurred 70 times.
---
LEVEL 1 : 3 ITEM PATTERNS
PATTERN: ALIMENTARY TRACT AND METABOLISM (A) -> ALIMENTARY TRACT AND METABOLISM (A) > ALIMENTARY TRACT AND METABOLISM (A)
PATTERN OCCURS 6 TIMES IN TOTAL OF 2 PATIENTS
44
OCCURENCES: (1, 63) (4, 1) (4, 2) (4, 3) (4, 15) (4, 16)
PATTERN: ALIMENTARY TRACT AND METABOLISM (A) -> ALIMENTARY TRACT AND METABOLISM (A) > CARDIOVASCULAR SYSTEM (C)
PATTERN OCCURS 11 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 39) (1, 56) (4, 4) (4, 9) (4, 12) (4, 22) (13, 9) (13, 15) (13, 25) (13, 34) (13,
45)
PATTERN: ALIMENTARY TRACT AND METABOLISM (A) -> CARDIOVASCULAR SYSTEM (C) ->
ALIMENTARY TRACT AND METABOLISM (A)
PATTERN OCCURS 12 TIMES IN TOTAL OF 5 PATIENTS
OCCURENCES: (1, 18) (1, 40) (1, 57) (3, 82) (4, 10) (4, 13) (4, 29) (4, 40) (12, 45) (13, 10) (13,
19) (13, 23)
PATTERN: ALIMENTARY TRACT AND METABOLISM (A) -> CARDIOVASCULAR SYSTEM (C) ->
CARDIOVASCULAR SYSTEM (C)
PATTERN OCCURS 9 TIMES IN TOTAL OF 4 PATIENTS
OCCURENCES: (1, 26) (4, 5) (11, 6) (11, 14) (11, 20) (11, 25) (11, 77) (11, 87) (13, 16)
PATTERN: ALIMENTARY TRACT AND METABOLISM (A) -> ANTIINFECTIVES FOR SYSTEMIC USE (J) ->
CARDIOVASCULAR SYSTEM (C)
PATTERN OCCURS 4 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 60) (9, 20) (13, 12) (13, 38)
PATTERN: CARDIOVASCULAR SYSTEM (C) -> ALIMENTARY TRACT AND METABOLISM (A) ->
ALIMENTARY TRACT AND METABOLISM (A)
PATTERN OCCURS 9 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 58) (1, 62) (4, 8) (4, 11) (4, 14) (13, 8) (13, 14) (13, 24) (13, 33)
PATTERN: CARDIOVASCULAR SYSTEM (C) -> ALIMENTARY TRACT AND METABOLISM (A) ->
CARDIOVASCULAR SYSTEM (C)
PATTERN OCCURS 7 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (4, 30) (4, 39) (4, 41) (11, 13) (11, 19) (11, 24) (13, 18)
PATTERN: CARDIOVASCULAR SYSTEM (C) -> ALIMENTARY TRACT AND METABOLISM (A) ->
ANTIINFECTIVES FOR SYSTEMIC USE (J)
PATTERN OCCURS 5 TIMES IN TOTAL OF 5 PATIENTS
OCCURENCES: (1, 41) (4, 26) (9, 19) (11, 2) (13, 11)
45
PATTERN: CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR SYSTEM (C) -> ALIMENTARY TRACT
AND METABOLISM (A)
PATTERN OCCURS 9 TIMES IN TOTAL OF 4 PATIENTS
OCCURENCES: (4, 7) (9, 18) (11, 1) (11, 23) (11, 35) (11, 54) (11, 66) (13, 7) (13, 17)
PATTERN: CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR
SYSTEM (C)
PATTERN OCCURS 31 TIMES IN TOTAL OF 7 PATIENTS
OCCURENCES: (1, 27) (3, 31) (3, 32) (3, 33) (3, 34) (3, 35) (4, 6) (5, 1) (5, 17) (9, 3) (9, 4) (9, 5)
(9, 6) (9, 7) (9, 8) (9, 9) (9, 17) (10, 7) (10, 11) (10, 19) (10, 20) (10, 21) (10, 22) (10, 28) (10, 37) (11, 7)
(11, 21) (11, 22) (11, 34) (11, 78) (11, 88)
PATTERN: CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR SYSTEM (C) -> SYSTEMIC HORMONAL
PREPARATIONS, EXCL. SEX HORMONES AND INSULINS (H)
PATTERN OCCURS 4 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 13) (5, 12) (5, 18) (11, 89)
PATTERN: CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR SYSTEM (C) -> MUSCULO-SKELETAL
SYSTEM (M)
PATTERN OCCURS 9 TIMES IN TOTAL OF 2 PATIENTS
OCCURENCES: (10, 8) (10, 12) (10, 23) (10, 29) (10, 38) (10, 41) (10, 47) (11, 26) (11, 79)
PATTERN: CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR SYSTEM (C) -> NERVOUS SYSTEM (N)
PATTERN OCCURS 9 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 28) (3, 2) (3, 22) (3, 25) (3, 36) (3, 45) (3, 94) (11, 8) (11, 15)
PATTERN: CARDIOVASCULAR SYSTEM (C) -> ANTIINFECTIVES FOR SYSTEMIC USE (J) ->
CARDIOVASCULAR SYSTEM (C)
PATTERN OCCURS 4 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (4, 24) (5, 3) (5, 15) (9, 1)
PATTERN: CARDIOVASCULAR SYSTEM (C) -> MUSCULO-SKELETAL SYSTEM (M) -> CARDIOVASCULAR
SYSTEM (C)
PATTERN OCCURS 9 TIMES IN TOTAL OF 4 PATIENTS
OCCURENCES: (7, 4) (10, 5) (10, 9) (10, 17) (10, 35) (10, 39) (10, 48) (11, 32) (12, 15)
PATTERN: CARDIOVASCULAR SYSTEM (C) -> NERVOUS SYSTEM (N) -> CARDIOVASCULAR SYSTEM (C)
PATTERN OCCURS 6 TIMES IN TOTAL OF 2 PATIENTS
46
OCCURENCES: (3, 15) (3, 17) (3, 23) (3, 29) (3, 92) (8, 2)
PATTERN: CARDIOVASCULAR SYSTEM (C) -> NERVOUS SYSTEM (N) -> NERVOUS SYSTEM (N)
PATTERN OCCURS 8 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (3, 3) (3, 12) (3, 19) (3, 37) (3, 46) (3, 64) (3, 87) (3, 95)
PATTERN: SYSTEMIC HORMONAL PREPARATIONS, EXCL. SEX HORMONES AND INSULINS (H) ->
NERVOUS SYSTEM (N) -> NERVOUS SYSTEM (N)
PATTERN OCCURS 4 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (3, 55) (6, 23) (6, 43) (12, 11)
PATTERN: ANTIINFECTIVES FOR SYSTEMIC USE (J) -> CARDIOVASCULAR SYSTEM (C) -> ALIMENTARY
TRACT AND METABOLISM (A)
PATTERN OCCURS 4 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 61) (4, 25) (4, 38) (13, 13)
PATTERN: MUSCULO-SKELETAL SYSTEM (M) -> CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR
SYSTEM (C)
PATTERN OCCURS 8 TIMES IN TOTAL OF 2 PATIENTS
OCCURENCES: (10, 1) (10, 6) (10, 10) (10, 18) (10, 27) (10, 36) (10, 40) (11, 33)
PATTERN: NERVOUS SYSTEM (N) -> CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR SYSTEM (C)
PATTERN OCCURS 7 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 12) (3, 21) (3, 24) (3, 30) (3, 44) (3, 93) (11, 47)
PATTERN: NERVOUS SYSTEM (N) -> CARDIOVASCULAR SYSTEM (C) -> ANTIINFECTIVES FOR SYSTEMIC
USE (J)
PATTERN OCCURS 3 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (5, 8) (6, 1) (12, 36)
PATTERN: NERVOUS SYSTEM (N) -> CARDIOVASCULAR SYSTEM (C) -> NERVOUS SYSTEM (N)
PATTERN OCCURS 7 TIMES IN TOTAL OF 2 PATIENTS
OCCURENCES: (3, 5) (3, 14) (3, 16) (3, 18) (3, 63) (3, 91) (8, 1)
PATTERN: NERVOUS SYSTEM (N) -> NERVOUS SYSTEM (N) -> CARDIOVASCULAR SYSTEM (C)
PATTERN OCCURS 7 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (3, 4) (3, 13) (3, 20) (3, 43) (3, 59) (3, 90) (3, 96)
47
PATTERN: NERVOUS SYSTEM (N) -> NERVOUS SYSTEM (N) -> NERVOUS SYSTEM (N)
PATTERN OCCURS 19 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (3, 41) (3, 42) (3, 47) (3, 48) (3, 49) (3, 50) (3, 51) (3, 52) (3, 56) (3, 57) (3, 58)
(3, 65) (3, 66) (3, 67) (3, 68) (3, 78) (3, 79) (3, 88) (3, 89)
--total of 211 patterns found.
total of 25 unique patterns.
the largest single pattern occurred 31 times.
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LEVEL 1 : 4 ITEM PATTERNS
PATTERN: ALIMENTARY TRACT AND METABOLISM (A) -> ALIMENTARY TRACT AND METABOLISM (A) > CARDIOVASCULAR SYSTEM (C) -> ALIMENTARY TRACT AND METABOLISM (A)
PATTERN OCCURS 5 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 39) (1, 56) (4, 9) (4, 12) (13, 9)
PATTERN: ALIMENTARY TRACT AND METABOLISM (A) -> CARDIOVASCULAR SYSTEM (C) ->
ALIMENTARY TRACT AND METABOLISM (A) -> ALIMENTARY TRACT AND METABOLISM (A)
PATTERN OCCURS 4 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 57) (4, 10) (4, 13) (13, 23)
PATTERN: ALIMENTARY TRACT AND METABOLISM (A) -> CARDIOVASCULAR SYSTEM (C) ->
CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR SYSTEM (C)
PATTERN OCCURS 6 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 26) (4, 5) (11, 6) (11, 20) (11, 77) (11, 87)
PATTERN: CARDIOVASCULAR SYSTEM (C) -> ALIMENTARY TRACT AND METABOLISM (A) ->
ALIMENTARY TRACT AND METABOLISM (A) -> CARDIOVASCULAR SYSTEM (C)
PATTERN OCCURS 6 TIMES IN TOTAL OF 2 PATIENTS
OCCURENCES: (4, 8) (4, 11) (13, 8) (13, 14) (13, 24) (13, 33)
48
PATTERN: CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR
SYSTEM (C) -> ALIMENTARY TRACT AND METABOLISM (A)
PATTERN OCCURS 4 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (4, 6) (9, 17) (11, 22) (11, 34)
PATTERN: CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR
SYSTEM (C) -> CARDIOVASCULAR SYSTEM (C)
PATTERN OCCURS 14 TIMES IN TOTAL OF 4 PATIENTS
OCCURENCES: (3, 31) (3, 32) (3, 33) (3, 34) (9, 3) (9, 4) (9, 5) (9, 6) (9, 7) (9, 8) (10, 19) (10,
20) (10, 21) (11, 21)
PATTERN: CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR
SYSTEM (C) -> MUSCULO-SKELETAL SYSTEM (M)
PATTERN OCCURS 6 TIMES IN TOTAL OF 2 PATIENTS
OCCURENCES: (10, 7) (10, 11) (10, 22) (10, 28) (10, 37) (11, 78)
PATTERN: CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR
SYSTEM (C) -> NERVOUS SYSTEM (N)
PATTERN OCCURS 3 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (1, 27) (3, 35) (11, 7)
PATTERN: CARDIOVASCULAR SYSTEM (C) -> MUSCULO-SKELETAL SYSTEM (M) -> CARDIOVASCULAR
SYSTEM (C) -> CARDIOVASCULAR SYSTEM (C)
PATTERN OCCURS 6 TIMES IN TOTAL OF 2 PATIENTS
OCCURENCES: (10, 5) (10, 9) (10, 17) (10, 35) (10, 39) (11, 32)
PATTERN: MUSCULO-SKELETAL SYSTEM (M) -> CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR
SYSTEM (C) -> CARDIOVASCULAR SYSTEM (C)
PATTERN OCCURS 6 TIMES IN TOTAL OF 2 PATIENTS
OCCURENCES: (10, 6) (10, 10) (10, 18) (10, 27) (10, 36) (11, 33)
PATTERN: NERVOUS SYSTEM (N) -> NERVOUS SYSTEM (N) -> NERVOUS SYSTEM (N) -> NERVOUS
SYSTEM (N)
PATTERN OCCURS 13 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (3, 41) (3, 47) (3, 48) (3, 49) (3, 50) (3, 51) (3, 56) (3, 57) (3, 65) (3, 66) (3, 67)
(3, 78) (3, 88)
49
--total of 73 patterns found.
total of 11 unique patterns.
the largest single pattern occurred 14 times.
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LEVEL 1 : 5 ITEM PATTERNS
PATTERN: CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR
SYSTEM (C) -> CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR SYSTEM (C)
PATTERN OCCURS 10 TIMES IN TOTAL OF 3 PATIENTS
OCCURENCES: (3, 31) (3, 32) (3, 33) (9, 3) (9, 4) (9, 5) (9, 6) (9, 7) (10, 19) (10, 20)
PATTERN: NERVOUS SYSTEM (N) -> NERVOUS SYSTEM (N) -> NERVOUS SYSTEM (N) -> NERVOUS
SYSTEM (N) -> NERVOUS SYSTEM (N)
PATTERN OCCURS 7 TIMES IN TOTAL OF 1 PATIENTS
OCCURENCES: (3, 47) (3, 48) (3, 49) (3, 50) (3, 56) (3, 65) (3, 66)
--total of 17 patterns found.
total of 2 unique patterns.
the largest single pattern occurred 10 times.
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LEVEL 1 : 6 ITEM PATTERNS
PATTERN: CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR
SYSTEM (C) -> CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR SYSTEM (C) -> CARDIOVASCULAR
SYSTEM (C)
PATTERN OCCURS 7 TIMES IN TOTAL OF 3 PATIENTS
50
OCCURENCES: (3, 31) (3, 32) (9, 3) (9, 4) (9, 5) (9, 6) (10, 19)
--total of 7 patterns found.
total of 1 unique patterns.
the largest single pattern occurred 7 times.
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51