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Data Mining & Cyberinfrastructures in
Biomedical Informatics
Ryan McGivern
CSE5095
May 1, 2011
Data Mining and Cyberinfrastructures in Biomedical Informatics - 1
Main Concepts
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Data Mining
 Knowledge Discovery
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Cyberinfrastructures
 Collaborative Research
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Nature of Biomedical Data
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Health
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care is more than numbers and readings
Can’t replace the subjective sense of disease
severity that a physician has in moments
Capture
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
data in a way that best captures observation
Data representation
Precision
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Review
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Medical datum
 Any single observation of a patient
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Knowledge
 Derived through formal/informal analysis of data
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Information
 Combine knowledge with data for new information
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 Heuristics and research models
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BMI Data-Knowledge Spectrum
 What information constitutes the substance of
medicine
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Nature of Biomedical Data
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Knowledge at one level of abstraction might be
considered data at another
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Medical Database is a Collection of individual patient
observations
 EHR is in some sense simply a database
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Using historical patient data from the EHR system can
facilitate the deduction of new knowledge related to
health care strategies
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Nature of Biomedical Data
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Humans can intuitively decompose information from
unitary view of data
 But nothing is intuitive to computational systems
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Example
 Clinical setting
 BP of 120/80 may suffice to indicate a normal reading
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Analytical setting
 Systolic BP = 120 mm Hg
 Diastolic BP = 80 mm Hg
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Nature of Biomedical Data
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Data mining in health is mainly related to Clinical
Research Support
 Clinical Data Repositories (CDRs)
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New knowledge learned through aggregated info from
a large number of patients
 Can be facilitated by EHRs
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Unfortunately
 CDRs generally limited to admin data sources
 Rarely store patient charts
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Nature of Biomedical Data
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CDRs support Clinical Research Studies
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Retrospective studies
 Investigate a hypothesis that was not a subject of
the study at the time the data were collected
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Prospective studies
 Clinical hypothesis known in advance
 Research protocol designed to collect future data
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Nature of Biomedical Data
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Knowledge base
 Facts
 Heuristics
 Complex models
 Semantic linking
 Conduct case based problem solving
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Medical data is intrinsically heterogeneous
 Illusory to conceive ‘complete medical dataset’
 Data selective based on treatment
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Data Mining in BMI
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Data mining
 Knowledge discovery technique
 Sophisticated statistical methods
 Identify trend patterns hidden amongst the sheer
size of the dataset
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Data warehouse
 Multiple heterogeneous data sources
 Organized under a unified schema
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Single site
Facilitate management and decision making
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CDR is essentially a data warehouse
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Architecture consists of four tiers
 External data sources
 Operational databases, flat files, etc.
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Data storage layer
 Unified schema, metadata, data marts
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OLAP Layer
 Data mining engine
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Presentation layer
 GUI
 Usually web-based
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Figure: Clinical Data Repository
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Data integration mechanism
 Extraction
 Transformation
 Refresh
 Scrubbing
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Data marts
 Subsets of data tailored to a user group
 Cache resultant datasets
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Data integration
 Heterogeneous data under a unified schema
 Ontologies
 Link primary data expressions to structured
vocabularies
 Data now available to search and algorithmic
processing at different levels of abstraction
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Clinical domain
 Notorious for overwhelming presence of natural
language text
 Natural language processing
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Data integration
 Cancer Biomedical Informatics Grid (caBIG)
 Seeks to integrate all cancer research data
 Standardize the way by which data is acquired,
formatted, processed, and stored
– Whole data ‘life cycle’
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Translational research
 No common architecture among vocabularies
 Therefore difficult to consolidate terms into a single
system
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Communication
 HL7
 Communication standard for exchange of all
information relevant to health care
 Focuses on meta-level of data integration within a
clinical setting
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Data Mining in BMI
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Online Analytical Processing Layer (OLAP)
 Formats aggregated data in multidimensional way
 Evaluated and visualized at presentation layer
 User specifies summary technique
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Data Cube
 Roll-up and drill-down operations
 Control abstraction level for each data dimension
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Data mining techniques
 Descriptive methods
 Mine for relationships among attribute types with as
few variables as possible
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Predictive methods
 Iterate through attributes and classify data into
predefined classes
 Identify similar classes

Other related methods
 Neural Networks
 Machine Learning

Each provides a way of recognizing data patterns
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UWV & VCU (2006)
 Data mining research
 667,00 digital records
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Duke University (1997)
 Perinatal outcomes
 45,922 patient records
Out-patient & in-patient
De-identified
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HealthMiner® (IBM)
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CliniMiner®
 Association analysis
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THOTH
 Predictive analysis
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215,626 encounters
3,898,887 lab results
217,453 procedures
3,016,313 physical findings
SQL Queries
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Average time 3 minutes
Longest time 12 minutes
 4 million records
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Challenges in mining biomedical data
 Non-hypothesis driven approaches
 Combinatorial explosion
 Degree of non-reducibility
– Minimize with sophisticated heuristics
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High dimensionality
 Sparse complex relationships
– Spread thinly across many dimensions
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Hypotheses
 Limit inherent bias in traditional clinical data analysis
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Challenges in warehousing biomedical data
 IT infrastructure for CDRs
 Established for clinical trials but separated from EHR
systems
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Data integration
 Map clinical terminologies to clinical research
standards
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Pseudonymization
 De-identification is a ‘must’ when EHR leaves the
realm of primary health care
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General road blocks
 Data sharing
 Researchers are protective of their data
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Language/vocabulary changes
 Due to required detail
 Bedside vs. laboratory
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Transdisciplinary research leads to competing
standards
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Advantages of mining biomedical data
 New health management strategies
 Relationships among patient observations
 Understanding of disease progression
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Undetected drug events
 Prevalence through larger sample populations
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Clinical trial cohort selection
 Identify patient types that will best prove a given
hypothesis
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Cyberinfrastructures in BMI
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Motivations
 Computer systems are now more than essential to
research
 Development of complex modeling tools
 But generally only available to a handful of clinical
researchers
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Integration of data from different disciplines
 Can require specialized training in mathematics,
statistics, and software
 Ideally want to provide a layer of abstraction that can
make this integration transparent to the researcher
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Cyberinfrastructures in BMI
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Mission
 Develop a geographically distributed virtual
research community that facilitates
 Data sharing
– Data warehousing
 Computational resource sharing
– Distributed grid computing
 Collaboration
– Research management
– Research protocol sharing
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Cyberinfrastructures in BMI
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Components of a cyberinfrastructure
 Data infrastructure
 Series of interconnected repositories
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Computational infrastructure
 Registered resource sharing
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Communication infrastructure
 Communication amongst architectures
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Human infrastructure
 Facilitate communication and collaboration between
registered researchers
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Cyberinfrastructures in BMI
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Data infrastructure
 Network of databases
 Facilitates remote storage, integration, and retrieval
of data
 Databases browsed by web based front-ends
 Can be extended to cater to
 Automatic acquisition
 Direct submission

Allows for pulling of data into local repositories
 For private or semi-private analyses
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Computational infrastructure
 Shared access to hardware and software
 Intensive computation needed for sophisticated analyses
– i.e. Image analysis software
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Essentially a computing grid
 Systems separated geographically but clustered over the
web
 Provides a virtual consolidated supercomputing node
 If system is idle locally, it is raised as a resource for
outsiders
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Communication infrastructure
 At the low level
 Require connectivity and acceptable bandwidth between
– Repositories
– Computational resources
– Researcher
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At the high level
 Responsible for maintaining syntactic and semantic
harmony throughout data
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Communication infrastructure continued
 Syntax and Semantics
 Suppose analysis involves data from different
repositories
 Syntactic connectivity established through a common
format for data organization
 Semantic connectivity maintains data interoperability by
ensuring concepts captured by the data share a common
terminology
– Usually implemented using an ontology
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Human infrastructure
 Ultimately, must facilitate the sociology of science
 Everyone curates communal data sets
 Encourage the sharing of
 Protocols
 Analysis algorithms
 Data sets

Similar to CICATS at UConn
 Research toolkit
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Human infrastructure continued
 Ideally researcher should be able to design
experiment at a high level
 Describe datasets, relationships, etc.
 Generally high level description language
– Workflow language
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Infrastructure then manages data retrieval,
analysis, and transformation
Constructs an environment where researchers can
get an in-depth result from a high level description
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There are many existing cyberinfrastructures
 Don’t necessarily implement all components
Most common form is an online database
 GenBank
 EMBL
 European Molecular Biology Lab
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UniProt
 Protein database
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PDB
 Protein data bank
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Online databases continued
 But, these lack the components to facilitate
 Collaboration
 Interdisciplinary research
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Use centralized resources and are generally
managed by the owning research group
Data centric
 Most of the computational architecture is dedicated
solely to data acess
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Community Annotation Hubs
 Open up a centralized database to direct
contribution from the research community
 SDSU Gene Wiki
 For the community annotation of gene function
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BMI Wikis have been recognized as some of the
most sophisticated document repositories
 Despite being a relatively recent umbrella discipline
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Still not a complete research environment
 Could be ‘plugged in’ to the human infrastructure of a
complete cyberinfrastructure
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Data sharing
 Still difficult to share data on disparate information
classes
 Even if they are related through a subset of attribute
types
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Further difficulty of interconnecting similar
repositories written by different research groups
 Differing technologies
 Differing data representations
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Reoccurring difficulty in integrating data
 Medical data is inherently heterogeneous
– Massive amount of data types involved
– Data is captured differently, because it’s used differently
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Data sharing challenge
 As Dr. Kevin Sullivan said in the discussion
 It is difficult for an institution to share their data
 It can be difficult to argue a business case to do so
– Institutions may not want people evaluating their treatments or
incorrect treatments
– Research groups get a sense of proprietary ownership over their
data
– Some institutions feel it is not theirs to share
– Others are skeptical as to how the community would react to
their health care provider exposing information to outsiders
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One interoperability solution is web services
 Provide common technology for heterogeneous
data and services to interoperate
 Common implementations consist of
 Web Service Description Language (WSDL)
– Describes capabilities of services
 Simple Object Access Protocol (SOAP)

Researchers never use services directly
 But rely on the analysis and visualization engines that
run on top of these
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Globus
 Open source libraries
 Industry heavyweight in web services for many
domains
 Provides mechanisms for
 Announcing the availability of a computer resource
 Discovering the resource
 Invoking the resource
Used by BIRN and caBIG
BioMOBY
 Similar to Globus but relatively lightweight
 Used by PlaNet Consortium
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Ontologies
 Web services allow heterogeneous data and
services to exchange
 But this does not enforce data semantics
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Ontologies are used to ensure an unambiguous
standard for data
Most BMI cyberinfrastructures specify ontologies
using OWL
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Biomedical Informatics Research Network (BIRN)
 Developed a robust software installation &
deployment system to implement a BIRN endpoint
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Host data and contribute computational resources
Access shared datasets through web portal
Analysis and visualization tools
Publish datasets through BIRN data repository
Roughly $20k for a BIRN rack
Technologies
 Globus: grid management
 BIRNLex: ontology
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Cancer Biomedical Informatics Grid
 Launched 2003
 Mission
 Provide a common information platform to support the
diverse clinical and basic research of the US National
Cancer Institute
– 87 cancer institutes at the time

Highly heterogeneous datasets
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Future of BMI cyberinfrastructures
 Use of cyberinfrastructure is growing rapidly
 Grid computing is increasingly more efficient
 Current weaknesses related to cross-discipline
collaboration
 Each implements an internally consistent grid, but
isolated from each other
 We need integration and communication among
disciplines to investigate further relationships
 Data interoperability may be resolved by semantic web

Current research in cyberinfrastructures is related
to using the semantic web concept
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Semantic web in cyberinfrastructures
 Web services make strong distinction between data
and data operations
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User identifies service to invoke
Formats the input data
Invokes the service
Unpacks and interprets the results
Semantic web is a technology tolerant of diverse
data models
 No data transformation services
 Just pieces of information and relationships between
them
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