IUPUI School of Computing and Informatics
S644 / I635 Consumer Health Informatics
Week 9: Knowledge Representation and Controlled Vocabularies
Contents
Week 9 Learning Goals and Objectives......................................................................................................... 1
Week 9 Introduction ..................................................................................................................................... 2
Week 9 Readings ........................................................................................................................................... 8
Week 9 Independent Learning Activities ...................................................................................................... 8
Week 9 Forum Discussion ............................................................................................................................. 9
Week 9 Learning Goals and Objectives

Define and describe relevant terminology and concepts including ‘controlled
vocabulary,’ ‘natural language,’ ‘keyword or text word,’ ‘metadata,’ ‘bibliographic
database,’ and others.

Explore a variety of consumer vocabulary initiatives including the UMLS and others.

Consider and discuss the ways in which consumer vocabulary initiatives impact on
the delivery of health information on the web.

Explore and describe the difficulties and challenges faced by patients, families, or
health consumers in using controlled vocabularies.

Describe the issues and challenges, and potential benefits and drawbacks in
adopting and using controlled vocabularies for CHI applications.
1
Week 9 Introduction
Week 9 ends Module C: Telemedicine Tools. During Module C (Weeks 6 - 9), our class
has explored a variety of consumer health websites, applied quality criterion to the
evaluation of consumer health resources. We have discussed the importance of quality
health information on the Internet or via mobile app, and recommended strategies for
ensuring quality of consumer health information.
You’ve learned a lot about quality assurance of consumer health resources! This week,
we conclude Module C with a discussion about knowledge representation and
consumer vocabularies.
Framing questions are in italics throughout the mini-lecture:
What is a consumer vocabulary? What is knowledge representation? How are these
issues relevant in CHI?
Standardized Medical Language Systems
Using a vocabulary imposes a formal language-based structure on the materials that
are included in the database of resources, and standardizes the ways in which
information is organized and searched, mined, and used.
In bibliographic databases (such as MEDLINE via OVID or PubMed), information
retrieval is additionally assisted by keyword matches on well-constructed resource
descriptions, or controlled vocabularies.
In the information sciences, the concept of ‘controlled vocabulary’ brings to mind
standardized ‘thesauri’ or ‘controlled vocabularies’ or ‘subject headings’ which are most
often used for subject indexing in bibliographic databases like MEDLINE (OVID or
PubMed), CINAHL, Psychological Abstracts, ERIC, and bibliographic databases of
publications in various fields. Through the indexing process, specific controlled
vocabulary terms are assigned to each journal article to represent its content.
For example, MEDLINE (OVID or PubMed) searchers perform subject searches, using
the MeSH (Medical Subject Headings) controlled vocabulary to retrieve relevant
articles. A sample MEDLINE citation, with MeSH highlighted appears below:
2
MeSH – Medical Subject Headings -- and other controlled vocabularies make it possible
to find relevant information by identifying subject headings (MeSH terms). Instead of
searching for keywords or text words or natural language words in the title, abstract, or
text of an article, searchers select the MeSH controlled vocabulary term that best
matches the concept for which they are searching. This dramatically improves
retrieval.
Other ontologies or controlled vocabularies are CLINICAL in nature – used in clinical
care to organize concepts for diagnosis, treatment, billing, electronic medical records,
and data mining. Clinical ontologies include ICD 9/10, DSM, and many others that are
field-specific.
A variety of controlled vocabularies or classification systems or ontologies or
thesauri with which you may be familiar include:
MeSH -- MEDLINE’s Medical Subject Headings
CINAHL Descriptors – CINAHL nursing and allied health database subject headings
ICD 9/10 -- International Statistical Classification of Diseases descriptors
DSM IV/V -- Diagnostic and Statistical Manual of Mental Disorders classifications
UMLS -- Unified Medical Language System
SNOMED -- Systematized Nomenclature of Medicine
As I said above, some of these ontologies are CLINICAL – used for coding treatment,
diagnoses, and for billing (DSM, ICD).
3
Others are used more strictly for information retrieval from bibliographic databases
(MeSH).
What other nomenclatures or controlled vocabularies are used in your fields? What are
the challenges and issues in using controlled vocabularies?
The UMLS – Unified Medical Language System – is an effort of the National Library
of Medicine of the National Institutes of Health to bring together a variety of ontologies
into one large resource. It is a meta-thesaurus. A meta-thesaurus includes terms from
many different thesauri. This is illustrated in the graphic below:
In nursing, as an example, significant efforts have been to create nursing language
systems such as thesauri and standardized classifications, to name and communicate
what nurses do, for what patient problems arise, and with what results. Coding and
specification of nursing language systems have been carried within the framework of
the Nursing Minimum Data Set (NMDS). Nursing diagnosis, nursing interventions, and
patient outcomes are among the nursing-specific elements of the NMDS.
The American Nurses Association (ANA) has formally recognized 13 nursing language
systems and is considering others. The Nursing Information and Data Set Evaluation
Center (NIDSEC) qualifies languages in relation to ANA criteria of usefulness in
supporting clinical practice. The language system must provide clinical useful terms and
a rational for development, consist of clear and unambiguous terms, and provide
evidence of reliability and validity.
4
Next, the way data are stored and made accessible for retrieval, and general system
performance characteristics such as performance and attention to security and
confidentiality are evaluated:
North American Nursing Diagnosis Association, Inc. (NANDA)
Nursing Interventions Classification System (NIC)
Nursing Outcomes Classification System (NOC)
Nursing Management Minimum Data Set (NMMDS)
Home Health Care Classifications (HHCC)
Omaha System
Patient Care Data Set (PCDS)
PeriOperative Nursing Dataset (PNDS)
SNOMED CT
Nursing Minimum Data Set (NMDS)
International Classification for Nursing (ICNP®)
ABCcodes
Logical Observation Identifier Names & Codes (LOINC®)
The above stands as an example of how specific fields have developed their own
ontologies. The American Dental Association has its own ontologies as well!
For the most part, these classification systems have been developed in parallel rather
than in concert, with differing results. Language systems are either developed for a
single purpose such as formalizing and expanding knowledge about professional
practice, (e.g., the North American Nursing Diagnosis Association Taxonomy
(NANDA)), or making explicit a profession’s role in health care, as with the Nursing
Intervention Classification (NIC), or the Nursing Outcome Classification (NOC). Some
language systems combines nursing diagnosis with interventions and outcomes for
particular settings such as Home Health Care Classification (HHCC) or are biased
toward single disciplines such as the Perioperative Nursing data Set (PNDS).
What impact do these initiatives and consumer vocabulary initiatives have on the
delivery consumer health information and consumer health informatics?
In contrast to MEDLINE and other highly-standardized bibliographic databases or
classification systems, the web/Internet offers no controlled vocabulary or universally
recognized search terminology. Instead, global search engines such as Yahoo, Google,
and others provide loosely organized search directories within which content is
organized according to broad categories. This process attempts to impose some simple
5
indexing structures. Unfortunately, and not unexpectedly, most search engines do not
conduct formalized content indexing. In addition, the ‘rough’ indexes that do exist are
not standardized across search engines. This makes looking for web-based information
haphazard at best.
Knowledge representation through the use of metadata is seen as a key to the
development of machine understandable information that will enable computer
software to perform intelligent searches, filter information automatically, or to tailor
information to the individual. The goal is for the web to transform into a global medical
knowledge base that is easily navigable and searchable across languages and
continents. …This is the goal, at any rate! Clearly, we’re a long way off.
How might this impact on CHI in practical application?
Recent developments in metadata and meta-thesaurus standards, and the invention of
eXtensible Markup Language (XML), Dublin Core metadata, MedPICS, and RDF
(Resource Description Framework) are designed to present syntax and semantics that
are compatible with HTML code, allowing web authors to tag their documents, ultimately
leading to more effective retrieval.
The Dublin Core Metadata initiative (DCMI), for example, spearheaded by OCLC (in
Dublin, Ohio), is working to establish international consensus on the syntax and
semantics of Internet-based content descriptions. DCMI promotes the widespread
adoption of interoperable metadata standards and the development of specialized
metadata vocabularies for describing resources that enable more intelligent information
discovery systems.
Other projects are creating ontologies based on natural language processing – taking
the words and concepts that are used by average people doing routine tasks or
searching on the web.
Consumer Vocabularies
Controlled vocabularies can also be discussed in the context of being within a website
where consumers discuss their symptoms openly with others that have a disease or
illness in common with them.
Natural language processing, often called Folksonomies or folksomic tags are used
to represent knowledge in sources and website such as PatientsLikeMe
(www.patientslikeme.com).
In one folksonomies project with PatientsLikeMe (Smith, 2008), each disease
community member was asked to track ten “core symptoms” of their condition. The core
list was first generated for the ALS (Lou Gehrig’s Disease) community with input from
healthcare practitioners and the indexed biomedical literature. Members could also
report, in natural language, any additional symptoms they were experiencing. The result
6
was a semi-structured alphabetical list which patients can use as an assist for future
symptom reporting.
Parts of a Vocabulary
Vocabularies typically consist of three high level components:
(1) Surface forms or structure such as alphanumeric strings;
(2) Underlying meanings representing distinct concepts; and
(3) Relationships between concepts (forms-concepts).
Yet, for a consumer health vocabulary to be useful it would, perhaps, have to be a
more simplified collection of health expressions used by actual consumers and linked
to professional concepts. This is the aim of consumer health vocabulary systems
projects are to bridge the gap between everyday language and medical language.
For example, there exists a consumer health vocabulary (CHV) initiative sponsored by
the University of Utah, Department of Biomedical Informatics
(http://consumerhealthvocab.chpc.utah.edu/CHVwiki/).
This initiative is based on the understanding that lay people do not often use complex
medical terminology, and certainly are not familiar with complex medical controlled
vocabularies like MeSH or SNOMED. This project bring together technical terms and
everyday terms so that lay people can use the terms with which they are familiar, but
still find content that uses the official terminology. For example, technical terms such
as "exanthema" can be translated into "rash," or “adaptation, psychological” can be
translated into “coping.”
The groundwork for the Utah consumer health vocabulary initiative began by mapping
commonly used consumer health expressions to the UMLS thesaurus. Then reviewers
(physicians, nurses, infomaticians, linguists, medical librarians, and patients) assessed
the mappings to UMLS concepts and consumer friendly display (names) for the term.
Entering the lay expression “ringing in the ear” as a search string returns the following,
for example:
CUI
Term
C0040264 tinnitus
CHV
Preferred
Name
UMLS
Preferred
Name
ringing in the
ear
tinnitus
Explanation of
Consumer Term
Disparaged
no
CUI: UMLS mapped concept ID
Term: the term as known to be found in text string by professionals
CHV Preferred Name: layman’s preferred name as defined in CHV, string
7
UMLS Preferred Name: preferred name for the CUI as defined by UMLS
The CHV (consumer health vocabulary) initiative has the underpinnings of principles
which were suggested by Zeng (2006): open access along with the three components
which connect the relationship between form and concepts. To date, this is exactly the
process through which consumer health vocabularies are creates; and the ULMS often
stands as the meta-thesaurus upon which the vocabularies are built.
So, the big question is: How does and will this impact on consumer health
informatics?
Zeng, Q., Tse, T., (2006) Exploring and developing consumer health
vocabularies. JAMIA. 13:24–29. DOI 10.1197/jamia.M1761
Smith, C. A. and P. J. Wicks (2008). PatientsLikeMe: Consumer
Health Vocabulary as a Folksonomy. AMIA Annual Symposium
Proceedings Archive
T.A. Bakker, A. N. Ryce, et al. (2005). A Consumer Health Informatics
(CHI) Toolbox: Challenges and Implications. AMIA Annual Symposium
Proceedings Archive.
Week 9 Readings

Lewis, Chapters 10 and 18

Computer-assisted update of a consumer health vocabulary through mining of
social network data. Doing-Harris KM. Zeng-Treitler Q. Journal of Medical
Internet Research. 13(2):e37, 2011.

Exploring relations among semantic groups: a comparison of concept cooccurrence in biomedical sources. Kandula S. Zeng-Treitler Q. Studies in Health
Technology & Informatics. 160(Pt 2):995-9, 2010.
Week 9 Independent Learning Activities

Read about the UMLS here:
http://www.nlm.nih.gov/research/umls/quickstart.html

Explore one other ontology system, and share what you’ve learned with
classmates!
8
Week 9 Forum Discussion
This week’s framing questions are listed here. Also refer back to our mini-lecture.
Why are controlled vocabularies important to information organization and searching?
Explore and describe the difficulties and challenges faced by patients, families, or health
consumers in using controlled vocabularies.
What are the key features of the UMLS? How are the UMLS vocabularies used inpractice and useful in theory?
How can our experiences with controlled vocabularies in bibliographic database
searching using highly controlled thesauri be applied to highly uncontrolled consumer
health tools?
Explore the ways in which consumer vocabulary initiatives may impact on the delivery of
health information on the web, via mobile apps, telehealth, and others.
9