New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontology and the future of Evidence-Based Medicine Dagstuhl May 23th, 2006 Werner Ceusters, MD Ontology Research Group Center of Excellence in Bioinformatics & Life Sciences SUNY at Buffalo, NY

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Evidence Based Medicine the integration of best research evidence with clinical expertise and patient values. –best research evidence: clinically relevant patient centered research into the accuracy and precision of diagnostic tests, the power of prognostic markers, and the efficacy and safety of care regimens. –clinical expertise: the ability to use clinical skills and past experience to rapidly identify each patient's unique health state. –patient values: the unique preferences, concerns and expectations each patient brings to a clinical encounter and which must be integrated into clinical decisions if they are to serve him.

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Application of Evidence Based Medicine Now: –Decisions based on (motivated/justified by) the outcomes of (reproducable) results of well-designed studies Guidelines and protocols –Evidence is hard to get, takes time to accumulate. Future: –Each discovered fact or expressed belief should instantly become available as contributing to ‘evidence’, wherever its description is generated.

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Future scenarios Data entered about a successful treatment of a case in X generates a suggestion for a similar case in Y; Submission of a new paper to Pubmed on some ADR triggers an alert in EHR systems worldwide for those patients that might be at risk; …  From reactive care to proactive care

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Some problem areas Pharmaceutical Industry: –Optimise drug discovery Make “messy” databases more useful for everybody Consumer health: –Opposing forces: Quality of information Make them consume Malpractice suits Public sector health: –Cost containment Cost effectiviness of treatment, prevention Bio-informatics world: –How to find out that a ‘discovery’ is a ‘new’ discovery ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U An action plan for a European eHealth Area. By the start of 2005: MS and EC should agree on an overall approach to benchmarking in order to assess the quantitative, including economic, and qualitative impacts of e-Health. By end 2006: in order to achieve a seamless exchange of health information across Europe through common structures and ontologies, MS, in collaboration with the EC, should identify and outline interoperability standards for health data messages and electronic health records, taking into account best practices and relevant standardisation efforts. By end 2008: the majority of all European health organisations and health regions (communities, counties, districts) should be able to provide online services such as teleconsultation (second medical opinion), e- prescription, e-referral, telemonitoring and telecare.

New York State Center of Excellence in Bioinformatics & Life Sciences R T U One key issue: Semantic Interoperability Working definition: –Two information systems are semantically interoperable if and only if each can carry out the tasks for which it was designed using data and information taken from the other as seemlessly as using its own data and information. system: Any organized assembly of resources and procedures united and regulated by interaction or interdependence to accomplish a set of specific functions. information system: a system, whether automated or manual, that comprises people, machines, and/or methods organized to collect, process, transmit, and disseminate data that represent user information.systemdata user information

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Essential components People: physicians, nurses, patients, healthcare administrators,... Machines: to make humans interact with the EHR, to transmit data from one EHR to another to enter data (lab analysers, EMR monitors,...) to interprete data (alerts, quality assessment, protocol selection,...) Data and information (data in context) Procedures Communication & Interpretation

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Understanding content (1) “John Doe has a pyogenic granuloma of the left thumb”    

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Understanding content (2) John Doe pyogenic granuloma of the left thumb   

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Understanding content (3) John Doe

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontology based on Unqualified Realism Accepts the existence of –a real world outside mind and language –a structure in that world prior to mind and language (universals / particulars) Rejects nominalism, conceptualism, ontology as a matter of agreement on ‘conceptualizations’ Uses reality as a benchmark for testing the quality of ontologies as artifacts by building appropriate logics with referential semantics (rather than model-theoretic)

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Relevance for EHR & Semantic Interoperability REALITYREALITY BELIEFBELIEF Ontology EHR The conceptualist approach

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Relevance for EHR & Semantic Interoperability REALITYREALITY Ontology EHR The realist approach L O G O L K A I S N S G

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Terminology A theory concerned with those aspects of the nature and the functions of language which permit the efficient representation and transmission of items of knowledge (J. Sager) Precise and appropriate terminologies provide important facilities for human communication (J. Gamper)

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontology An ontology is a representation of some pre-existing domain of reality which –(1) reflects the properties of the objects within its domain in such a way that there obtains a systematic correlation between reality and the representation itself, –(2) is intelligible to a domain expert –(3) is formalized in a way that allows it to support automatic information processing

New York State Center of Excellence in Bioinformatics & Life Sciences R T U A division of labour Terminology: –Communication amongst humans –Communication between human and machine Ontology: –Representation of “reality” inside a machine –Communication amongst machines –Interpretation by machines

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Today’s biggest problem: a confusion between “terminology” and “ontology” The conditions to be agreed upon when to use a certain term to denote an entity, are often different than the conditions which make an entity what it is. –Trees would still be different from rabbits if there were no humans to agree on how these things should be called. “ontos” means “being”. The link with reality tends to be forgotten: one concentrates on the models instead of on the reality.

New York State Center of Excellence in Bioinformatics & Life Sciences R T U What to do about it ? (1) Research: –Revision of the appropriatness of concept-based terminology for our purposes –Relationship between models and that part of reality that the models want to represent –Adequacy of current tools and languages for representation –Boundaries between terminology and ontology and the place of each in semantic interoperability in healthcare

New York State Center of Excellence in Bioinformatics & Life Sciences R T U What to do about it ? (2) Training and awareness –Make people more critical wrt terminology and ontology promisses What is needed must be based on needs, not on the popularity of a new concept But in a system, it’s not just your own needs, it is each component’s needs ! –Towards “an ontology of ontologies” First description Then quality criteria

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ultimate goal #IUI-1 ‘affects’ #IUI-2 #IUI-3 ‘affects’ #IUI-2 #IUI-1 ‘causes’ #IUI-3... Referent Tracking Database EHR CAG repeat Juvenile HD person disorder continuant Ontology

New York State Center of Excellence in Bioinformatics & Life Sciences R T U 3 fundamentally different in levels 1.the reality on the side of the patient; 2.the cognitive representations of this reality embodied in observations and interpretations on the part of clinicians and others; 3.the publicly accessible concretizations of such cognitive representations in representational artifacts of various sorts, of which ontologies and terminologies are examples.

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Example: a person (in this room) ’s phenotypic gender Reality: –Male –Female Cognitive representation –[male] –[female] In the EHR: –“male” –“female” –“unknown” Other types of phenotypic gender ?

New York State Center of Excellence in Bioinformatics & Life Sciences R T U 4 fundamental reasons for making changes 1.changes in the underlying reality does the appearance of an entry (in a new version of an ontology or in an EHR) relate to the appearance of an entity or a relationship among entities in reality ?; 2.changes in our (scientific) understanding; 3.reassessments of what is considered to be relevant for inclusion (notion of purpose), or: 4.encoding mistakes introduced during data entry or ontology development.

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Key requirement Any change in an ontology or data repository should be associated with the reason for that change !

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Example: a person (in this room) ’s gender in the EHR In John Smith’s EHR: –At t 1 : “male”at t 2 : “female” What are the possibilities ? Change in reality: transgender surgery Change in understanding: it was female from the very beginning but interpreted wrongly ( No change in relevance ) Correction of data entry mistake (was understood as male, but wrongly transcribed)

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Possible combinations OE: objective existence; ORV: objective relevance; BE: belief in existence; BRV: belief in relevance; Int.: intended encoding; Ref.: manner in which the expression refers; G: typology which results when the factor of external reality is ignored. E: number of errors when measured against the benchmark of reality. P/A: presence/absence of term.

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Possible evolutions

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Towards an implementation A client-server application in which the server is composed of four layers: –the Web Server Layer (WSL) provides the interface to clients via web services; –the RT Core application programming interface (API) encapsulates the data services related to storage and retrieval. Its Security Module validates the access rights before any data service; –the database layer stores all the RT data, and; –the reasoner layer (RL) performs inferences upon specific requests, based on the information available in the database and, if available, the ontologies that have been used for the descriptions of the portions of reality. Shahid Manzoor

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Schematic representation Inte rne t Referent Tracking (RT) Server Web Server Referent Tracking Web services Referent Tracking Core System API Security Module  RT Data  Imported Ontologies rules Reasoner Health Institution B registered WITH RT EHR Client Health Institution A hosting RT EHR Client Session Manager

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Simple Graph Representation Privacy issues

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Complete structure

New York State Center of Excellence in Bioinformatics & Life Sciences R T U UML-diagram for the entities in the RDF-graph

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Querying the RTDB using ontologies ( SPARQL ) 1 PREFIX rts: 2 PREFIX fma: 3 SELECT ?p ?u ?v 4 WHERE {?p rts:relation ?u. 5 ?u a rts:PtoU. 6 ?u rts:u ?t. 7 ?t a fma:Face. 8 } Retrieve particulars that are related to the universal face

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Querying the RTDB using ontologies ( SPARQL ) 1 WHERE {?p rts:relation ?rf. 2 ?rf a rts:PtoP. 3 ?rf rts:p ?f. 4 ?f a fma:Head. 5 ?f rts:relation ?rd. 6 ?rd a rts:PtoP. 7 ?rd :p ?d. 8 ?d a dis:DISEASES AND INJURIES. } Retrieve patients with diseases in the head

New York State Center of Excellence in Bioinformatics & Life Sciences R T U Test interface