On the use of Semantic Technologies in the Health, Medical and Biomedical Domains
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On the use of Semantic Technologies in the Health, Medical and Biomedical Domains

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Presentation by Cédric Pruski at the Seminar on Semantic Technologies, Tudor Research Centre, Luxembourg, 21/03/2011

Presentation by Cédric Pruski at the Seminar on Semantic Technologies, Tudor Research Centre, Luxembourg, 21/03/2011

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  • In addition to precision of meaning, consistancy, understandability and reproducibility are three major desiderata for semantically interoperable systems: - Consistancy means that the receiving system must be able to recognise what has been sent, so it is a prime requirement for machine-machine communications and dictates the need for unambiguous identifi ers. - Understandability is essential for human communication. Humans can tolerate considerable ambiguity, but tend to focus too narrowly, so that the requirements are almost the reverse as for automated support. It is limited by the trust that the information is valid, especially with aggregated population data where the aggregation process may result in loss of information. - Reproducibility addresses the question of inter-individual reliability when data are collected or encoded. Th is holds both for individual and aggregated data.

Transcript

  • 1. On the use of Semantic Technologies in the Health, Medical and Biomedical Domains. Cédric Pruski, CR SANTEC - CRP Henri Tudor, March 21 st 2011, Luxembourg
  • 2. 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
  • 3. 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
  • 4. 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
  • 5. 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
  • 6. Semantic Tech. for Interoperability: The eSanté platform eSanté platform EHR LOINC Code: 3084-1 Data producers Data consumers Images LABO Measurement of urate in serum or plasma, result expressed in mg/dl Labo Report Patient: … Prescribed exam: … Conclusion: …
  • 7. 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
  • 8. 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
  • 9. Semantic Tech. for Data Integration and Retrieval: Application in biobanking
  • 10. 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
  • 11. Semantic Tech. for Decision Support: Personalization of treatments CIG Specification Approach Terminologies use CI Guideline follow CIG Engine execute Health Care Professionals Policies Patient Constraints Care Inst. Policies and Constraints refine constrain Treatment Interactions Drugs Interactions refine and implement part-of Careflow Engine execute use constrain constrain constrain use * * * * * * * * * * * * * * * * * * * * 1 . .* * * 1 Patient Records constrain * * 1 * * 1 1 use Patterns use follow 1 1 1 * Care Inst. Clinical Protocol Patient Careflow Enriched CIG
  • 12. 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
  • 13. Contact: [email_address] www.santec.lu