1 Integrating Bio and Health Informatics: Ontologies for Bridging Scales, Contexts and Customs Integrating Bio and Health Informatics: Ontologies for Bridging Scales, Contexts and Customs Alan Rector Bio and Health Informatics Forum/ Medical Informatics Group Department of Computer Science University of Manchester img.man.ac.uk mygrid.man.ac.uk

2 Organisation of Talk Convergence of needs and technology Barriers A response: the CLEF programme A unifying issue: Ontologies & Information Integration Summary

3 The Problem The next steps in exploiting our exploding knowledge of basic biology depends on understanding its relation with health and disease. Health care is –Deluged with information about generalities, policies, and theory –Information and Knowledge Poor about specifics of patient care and outcomes

4 A Convergence of Need Post genomic research Knowledge is Fractal Safe, high quality, evidence based health care Need more and better clinical information Which scales –In Size –In Complexity

5 A convergence of Technologies Web/Grid/Semantic Web Ontologies & Information fusion Language technology Data mining and case based reasoning Healthcare records & standards Mobile devices Post genomic research Safe, high quality, evidence based health care Open Collaborative Research

6 A Unique Time E-Science The Grid The Semantic Web / Grid BioInformatics Genomics/Proteomics… Massive investment in population medicine Massive investment in NHS computing Maturing Electronic Health Records … Ride the Whirlwind!

7 Protocol/Collection-based research Results in vivo Research idea Protocol Authoring Tools Data Collection Tools Shared Collections Models & Standards Protocol Approval Tools Automatic Patient Screening Data Analysis Tools Plausibility in Silico/Collecto

8 Accelerating the Knowledge Cycle Improving Quality of Care HypothesesDesign Anonymisation Analysis & Integration Annotation / Knowledge Representation Info Sources Anonymised Repository & Workbench Information Fusion Clinical Results Individualised Medicine Data Mining Case-Base Reasoning Data Capture Language Image/Signal Genomic/Proteomic Libraries Re-use Patient Care E lectronic P atient R ecords

9 An opportunity for E-Science / E-Health

10 What is e-Science ‘e-Science is about global collaboration in key areas of science, and the next generation of infrastructure that will enable it.’ ‘e-Science will change the dynamic of the way science is undertaken.’ John Taylor Director General of Research Councils Office of Science and Technology

11 The Semantic Web / Grid “The Semantic Web is a vision: the idea of having data on the web defined and linked in a way, that it can be used by machines - not just for display purposes, but for using it in various applications.” “Our vision of the infrastructure that is needed to support the full richness of the e-Science vision draws on research and development in both the Grid and the Semantic Web, and adopts a service- oriented approach. We call it the Semantic Grid.”

12 Emphasize co-operation rather than sheer size –“Virtual organisations” –“Colaboratories” –“Collection based science” Layered model –Organisational layer –Knowledge layer –Information layer –Computational/data layer UK e-Science/Grid Activities my Grid CLEF National e-Science Centre

13 but…“Stones in the Road” Confidentiality, Privacy and Consent –Keeping public confidence while enabling research Information capture –Speed and ease of use require language technology Information integration –Need common ontologies to bridge bio and health information

14 Social Policy Imperative Confidentiality, Privacy, and Consent Keeping public confidence while enabling research –Balance individual risks and societal benefits Social policy research –Evidence-based debate rather than dogmatic disputation Technical means for enforcement –Grid & web infrastructure not yet adequate Potential Show Stopper Good practice rigorously observed & sensibly enforced

15 The Information Capture Bottleneck What clinicians have heard, seen, thought, & done Speed & ease of use for entering clinicians –Care can’t wait –Training opportunities minimal Language technology –Doctors dictate; nurses write; annotators annotate Quality –Much current information is unreliable possibly even dangerous Information from people rather than machines…

16 Information Integration Bottleneck “Joining up meaning” Differences in concepts –“What is a gene?” –“What is a diseases?” Differences in purpose & relevance –Clinical care vs clinical research Differences in context –Mouse vs human anatomy Differences in granularity –Genetic, genomic, … organ…organism

17 One response: CLEF “Clinical E-Science Framework” A Demonstrator in Cancer Care & Research

18 CLEF Towards and “end-to-end” solution in an ethical framework Patient care Formulation of clinical studies Information capture Information representation Information analysis and integration Knowledge & hypothesis generation Clinical support

19 CLEF: A meeting of open technologies Organisational issues & Information governance –Consent, Models of access, balance of research and privacy Information capture & quality –Language technology + Ontologies + E Health Record (OpenEHR) Information use for Care –E Health Record + Decision support + Ontologies + Language generation Information Re-use for Research –Pseudonymised E Health Record + Ontologies + Metadata/repositories

20 CLEF: Language Technology Extraction of simple information from clinical records –Measures of reliability Pseudonomysation aids Language generation –Validation “What you see is what you meant” –Presentation

21 CLEF: An attempt at the possible Maximising –improvement in information quantity, quality, and reliability Minimising –Changes in clinicians’ behaviour –Additional costs Scaling –Developing in specialist centres –Testing in routine care

22 CLEF: Information Integration The role of ontologies

23 Ontologies and Knowledge Resources The common conceptualisation of a field –Common language; common facts Anatomy, physiology, structure, drugs, sequences, SNPs For… –Integration & Information fusion Linking resources –Indexing The right information at the right time –Annotation and Meta data Significance + Information  Meaning A Common resource requiring Common Effort & Common Tools in Common Use

24 Logic-based Ontologies: Conceptual Lego “ SNPolymorphism of CFTRGene causing Defect in MembraneTransport of ChlorideIon causing Increase in Viscosity of Mucus in CysticFibrosis …” “Hand which is anatomically normal”

25 Encrustation + involves: MitralValve Thing + feature: pathological Structure + feature: pathological + involves: Heart Logic Based Ontologies: The basics Thing Structure HeartMitralValveEncrustation MitralValve * ALWAYS partOf: Heart Encrustation * ALWAYS feature: pathological Feature pathological red + (feature: pathological) red + partOf: Heart red + partOf: Heart PrimitivesDescriptionsDefinitionsReasoning Validating (constraining cross products)

26 Bridging Scales with Ontologies Genes Species Protein Function Disease Protein coded by (CFTRgene & in humans) Membrane transport mediated by (Protein coded by (CFTRgene in humans)) Disease caused by (abnormality in (Membrane transport mediated by (Protein coded by (CTFR gene & in humans)))) CFTRGene in humans

27 Avoiding combinatorial explosions The “Exploding Bicycle” From “phrase book” to “dictionary + grammar” – ICD-9 (E826) 8 – READ-2 (T30..) 81 – READ-3 87 – ICD-10 (V10-19 Australian) 587 V31.22 Occupant of three-wheeled motor vehicle injured in collision with pedal cycle, person on outside of vehicle, nontraffic accident, while working for income –and meanwhile elsewhere in ICD-10 W65.40 Drowning and submersion while in bath-tub, street and highway, while engaged in sports activity X35.44 Victim of volcanic eruption, street and highway, while resting, sleeping, eating or engaging in other vital activities

28 The Cost: Normalising (untangling) Ontologies Structure Function Part-whole Structure Function Part-whole

29 The Cost: Normalising (untangling) Ontologies Making each meaning explicit and separate PhysSubstance Protein ProteinHormone Insulin Enzyme Steroid SteroidHormone Hormone ProteinHormone^ Insulin^ SteroidHormone^ Catalyst Enzyme^ Hormone = Substance & playsRole-HormoneRole ProteinHormone = Protein & playsRole-HormoneRole SteroidHormone = Steroid & playsRole-HormoneRole Catalyst =Substance & playsRole CatalystRole Insulin  playsRole HormoneRole …build it all by combining simple trees Enzyme ?=? Protein & playsRole-CatalystRole PhysSubstance Protein ‘ ProteinHormone’ Insulin ‘Enzyme’ Steroid ‘SteroidHormone’ ‘Hormone’ ‘ProteinHormone’ Insulin^ ‘SteroidHormone’ ‘Catalyst’ ‘Enzyme’ … ActionRole PhysiologicRole HormoneRole CatalystRole … … Substance BodySubstance Protein Insulin Steroid …

30 Distributed cooperative development: Developed & owned by the community Tools to –Hide complexity –Guide domain experts –Re-use common resources –Track provenance Self-training and support for users “Just in time terminology” –Distributed loosely coupled development

31 CLEF: Re-Use & Integration Pseudonymised longitudinal repository –Fine grained security Authorisation and Consent –Integrated clinical, genomics, imaging information What happened? When? Why? What was done? When? Why? Clinical E-Science Workstation –Common access at varying levels of aggregation –Human Factors – Bio-Clinical problem solving –What are the high value scientific questions

32 Summary Convergence of need in healthcare & post genomic research –Matched by convergence of technologies E-Science – an opportunity for collaboration –Faster, less costly, more effective translation from bioscience to health care Barriers to be overcome –Privacy, confidentiality, & consent –Information capture –Information integration – sharing of meaning Common “Ontologies” are a key resource

33 CLEF Consortium: Academic & NHS Partners Bio Health Informatics Forum, Department of Computer Science, University of Manchester Centre for Health Informatics and Multiprofessional Education, University College London Natural Langauge Group, Department of Computer Science, University of Sheffield Judge Institute for Management Studies, University of Cambridge Information Technology Research Institute, University of Brighton Royal Marsden Hospital Trust North and North Central London Cancer Networks