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Semantic Retrieval of EMR
Semantic Retrieval of Medical Records
Related to Patient Symptoms
Huimin Zhao
(Joint work with Hemant K. Jain, David
P. Klemer, and Carmelo Gaudioso)
University of Wisconsin – Milwaukee
Winter IS 2006, Salt Lake City
Jain, Zhao, Klemer, & Gaudioso
UWM
Semantic Retrieval of EMR
Outline
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Need for Symptom-based EMR Retrieval
Related Work
Proposed Framework
Major Challenges and Research Issues
Research Plan
Jain, Zhao, Klemer, & Gaudioso
UWM
Semantic Retrieval of EMR
Movements towards EMR
> Having EMR in doctor's offices and hospitals is
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both crucial and long overdue
There are many active movements towards EMR
> “The Decade of Health Information Technology: Delivering
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Consumer-centric and Information-rich Health Care"
Master Patient Index (MPI) research funded by the National
Institute of Standards
Integrating the Healthcare Enterprise (IHE) initiative
sponsored by RSNA and HIMSS
IBHIS in the UK
CHIN in Europe
Jain, Zhao, Klemer, & Gaudioso
UWM
Semantic Retrieval of EMR
Some Major EMR Systems
> Vista
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> Used by the military and VA facilities
> Office version will be offered to all doctors free of charge
Cerner
> Used by Aurora
Epic
> Used by Advanced Healthcare (network of 250 physicians
in Southeast Wisconsin)
> GE
Jain, Zhao, Klemer, & Gaudioso
UWM
Semantic Retrieval of EMR
Need for Symptom-based Retrieval of EMR
> Next challenge will be on effective EMR retrieval
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> Size of the EMR databases is enormous
> Largely skewed distribution; "90 - 10" rule
The current query/search functions in EMR
systems are limited
An intelligent retrieval system helps doctors
identify EMRs relevant to a patient's symptoms
Jain, Zhao, Klemer, & Gaudioso
UWM
Semantic Retrieval of EMR
Commonly-Encountered Scenarios
> Emergency room encounters a patient for the
first time, presenting with particular symptoms
> The patient’s relevant medical history has significant
impact on the approach to diagnosis and treatment
> Family practitioner sees a new patient, who
needs immediate refills for heart medications
> Very specific subset of the patient’s records
(medications and doses) is needed
> Records related to possible condition needs to
be accessed while unrelated records ignored
Jain, Zhao, Klemer, & Gaudioso
UWM
Semantic Retrieval of EMR
Related Work
> Simply word based Information Retrieval (IR)
may not yield satisfactory performance,
> due to the complex semantic relationships among
symptoms and diagnostic/therapeutic processes
> Enhancing free-text queries
> using medical lexicons, semantic relationships, and
domain knowledge
> Most studies devoted to retrieving general
medical documents, rather than EMR
Jain, Zhao, Klemer, & Gaudioso
UWM
Semantic Retrieval of EMR
Methods for Query Refinement
> EMR retrieval tools
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> Medical lexicons (Baud et al. 2001)
> Clinical Terms Version 3 (Brown and Sonksen 2000)
Medical literature search tools
> Domain-specific vocabularies (askMEDLINE, Fontelo et al.
2005)
> UMLS (Medical World Search, Suarez et al. 1997; Leroy
and Chen 2001; Plovnick and Zeng 2004; Liu and Chu 2005)
> MeSH (Malet et al. 1999; Göbel et al 2001)
Jain, Zhao, Klemer, & Gaudioso
UWM
Semantic Retrieval of EMR
Shortcomings in Existing Systems
> Existing systems have used some semantic
relationships (e.g., synonyms, hyper/hyponyms)
in standard ontologies
> Mismatches due to terminology discrepancies are
reduced.
> More complex relationships across symptoms,
diagnoses, and therapeutics are not used
> Cross references are not handled.
Jain, Zhao, Klemer, & Gaudioso
UWM
Semantic Retrieval of EMR
Preliminary Proposed Framework
Jain, Zhao, Klemer, & Gaudioso
UWM
Semantic Retrieval of EMR
Knowledge Base: the Key
> Healthcare ontologies.
> Dictionary, lexicon, thesaurus, terminology,
classification, semantic network
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> E.g., Unified Medical Language System (UMLS)
Domain knowledge elicited from experts
> Both global and local knowledge
Other heuristics
> E.g., term co-occurrence and relevance feedback
Jain, Zhao, Klemer, & Gaudioso
UWM
Semantic Retrieval of EMR
UMLS
> A leading medical ontology developed by the
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National Library of Medicine (NLM).
UMLS Knowledge Source Server supports
online queries and Java APIs.
Consists of three knowledge sources
> Metathesaurus
> Semantic Network
> SPECIALIST lexicon
Jain, Zhao, Klemer, & Gaudioso
UWM
Semantic Retrieval of EMR
UMLS Knowledge Source Server
Jain, Zhao, Klemer, & Gaudioso
UWM
Semantic Retrieval of EMR
Metathesaurus
> Consists of over 60 source vocabularies,
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including ICD, MeSH, SNOMED.
Contains synonyms, narrower concepts
(hyponyms), and broader concepts
(hypernyms), about medical concepts
E.g., "colon cancer”
> Synonyms: “colorectal cancer”, “colon carcinoma”
> Hyponyms: "malignant neoplasm of hepatic flexure of colon”
> Hypernyms: “malignant neoplasms”
> Can be directly used to expand a user query.
Jain, Zhao, Klemer, & Gaudioso
UWM
Semantic Retrieval of EMR
Co-occurrence Statistics
> Metathesaurus also contains term co>
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occurrence info based on contributing sources
> E.g., “colon”, “intestinal mucosa”, “rectum”, etc.
Can be used as initial heuristics of term cooccurrence, refined in EMR databases
Can be used as hypothetical related concepts in
the domain knowledge base
Jain, Zhao, Klemer, & Gaudioso
UWM
Semantic Retrieval of EMR
Semantic Network
> Categorizes concepts in the Metathesaurus and
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relationships among concepts.
> 155 semantic types
> 54 relationships
> E.g., “Antibiotic” “treats” “Disease or Syndrome”
Schema only, no instances
Can be used to design the schema of the
knowledge base
Jain, Zhao, Klemer, & Gaudioso
UWM
Semantic Retrieval of EMR
SPECIALIST Lexicon
> Contains syntactic, morphological, and
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orthographic information about medical terms
> E.g., pain, pains, pained, paining
Can be used to standardize different forms of a
term into a uniform basic form
Jain, Zhao, Klemer, & Gaudioso
UWM
Semantic Retrieval of EMR
Major Challenges and Research Issues
> Heterogeneity across local systems
> Integration with Web services, industry standards (e.g., HL7)
> Diversity of media types
> Structured data, free text, and images (X-ray,
cardiology, ultrasound), handwritten reports
> The knowledge acquisition bottleneck
> Knowledge engineering on global and local levels
> Evolution of domain knowledge
> Knowledge base needs to adapt to changes
> Privacy, confidentiality, and security
> Adequate policies, procedures, and technologies needed.
Jain, Zhao, Klemer, & Gaudioso
UWM
Semantic Retrieval of EMR
Current Status
> This research is still in an early planning stage
> Formed an interdisciplinary research team, with
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IS researchers and healthcare experts
Formulated a preliminary framework
Installed Vista-Office and UMLS access
Jain, Zhao, Klemer, & Gaudioso
UWM
Semantic Retrieval of EMR
Ongoing Work
> Seeking collaboration with Advanced Healthcare
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and Independent Physician Organization.
Designing a prototype system
Designing a knowledge base for a few sectors
(e.g., heart disease, diabetes, asthma)
Will extend the knowledge base to other areas
Applying for internal and external grants
Jain, Zhao, Klemer, & Gaudioso
UWM
Semantic Retrieval of EMR
Comments !
Jain, Zhao, Klemer, & Gaudioso
UWM