on the use of semantic technologies in the health, medical and biomedical domains
DESCRIPTION
Presentation by Cédric Pruski at the Seminar on Semantic Technologies, Tudor Research Centre, Luxembourg, 21/03/2011TRANSCRIPT
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On the use of Semantic Technologies in the Health, Medical and Biomedical Domains.
Cédric Pruski, CR SANTEC - CRP Henri Tudor,
March 21st 2011,
Luxembourg
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Problems
Explosion of the quantity of health, medical and biomedical information Electronic Health Records,
Impossible for human to process it in an efficient way
Located in various places and generated by different systems Hospitals, Physicians practices, laboratories, biobanks
Need to be used in an integrated way Information expressed in various languages
Need to be unambiguously interpreted by human and machines
Motivations
Improve health care Enhancing the management of medical data, Facilitate data exchange and interpretation between the
various actors and systems, Develop Decision Support System to assist health
professionals in their daily practice, Accompanying patients through their therapy
Reduce costs Limiting the number of exams by making the most of the
available information
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Agenda
Semantic Technologies for Interoperability The eSanté platform example
Semantic Technologies for Medical and Biomedical Information Integration and Retrieval An application in biobanking
Semantic Technologies for Decision Support Personalization of medical treatments
Research issues and future work
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Semantic Tech. for Interoperability
It addresses issues of how to best facilitate the coding, transmission and use of meaning across seamless health services, between providers, patients, citizens and authorities, research and training.
Its geographic scope concerns: local interoperability (e.g., hospitals or hospital networks) regional, national and cross border interoperability.
The information transferred may be at the level of: Patients: EHR, Public health: health economics, surveillance, bio- and tissue-
banking, epidemiology
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Semantic Tech. for Interoperability: The eSanté platform
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Images
LABO
eSanté platform
EHR
Measurement of urate in serum or plasma, result expressed in mg/dl
Labo Report
Patient: …Prescribed exam: …
Conclusion: …
LOINC Code: 3084-1
Data producers
Data consumers
Semantic Tech. for Interoperability: The eSanté platform
Advantages: Search only for relevant information Makes it possible to compare results Every actors will have the same understanding of the produced
information
Drawbacks: Constant evolution
New exams will require new codes Codes are refined according to requirement
Heterogeneity in the used Knowledge Organizing System (KOS)
Model expressivity, Formalism
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Semantic Tech. for Data Integration and Retrieval
Biomedical data is distributed, heterogeneous and sometimes incomplete and/or duplicated
Use of KOS to combine such kind of data
KOS enables: Unambiguous identification of entities in heterogeneous
information systems and assertion of applicable named relationships that connect these entities together
Accurate interpretation of data from multiple sources through the explicit definition of terms and relationships in the KOS
Retrieval of relevant information by supporting the construction of “good” queries
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Semantic Tech. for Data Integration and Retrieval: Application in biobanking
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Semantic Tech. for Decision Support
Data enriched in semantics requires appropriate concepts and tools to be exploited
The quantity of information is not human processable anymore Development of Decision Support Systems (DSS) Reasoners
DSS require the use of formal language for reasoning purposes Web Ontology Language (OWL) Description Logics
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Semantic Tech. for Decision Support: Personalization of treatments
CIG Specification
Approach
Terminologies
use
CI Guideline
followCIGEngine
execute
Health Care Professionals
Policies
PatientConstraints
Care Inst. Policies and Constraints
Care Inst. Clinical
Protocol
refine
constrain
PatientCareflow
Treatment Interactions
Drugs Interactions
refine and implement
part-of
CareflowEngine
execute
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Enriched CIG
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Patterns
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Research Perspectives
Evolution of medical knowledge Needs for new approaches for driving the evolution of KOS
taking into account: The underlying knowledge representation model The specificities of the evolution of knowledge The propagation of changes to all depending artifacts The validation of the modifications
Alignment of medical knowledge The size of the medical domain requires the use of several
KOS Heterogeneity in the KOS models makes alignment complex Mapping maintenance when KOS evolve
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