1 Joined up Health and Bio Informatics: Joined up Health and Bio Informatics: 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 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

3 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

4 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

5 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!

6 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

7 “Stones in the Road” Confidentiality, Privacy and Consent –How to keep public confidence while enabling research Information capture –Speed and ease of use require language technology doctors dictate! Information integration –Need common ontologies which bridge bio and health information

8 One Response: CLEF Joining up Health Care & Bioscience in Cancer

9 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

10 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 (OpenGALEN & OWL) + 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

11 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

12 CLEF 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” OpenGALEN & OWL

13 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

14 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

15 Making it simple: Tools Logic based ontology (OWL) is the assembler –Write real ontologies in “high level languages” “Intermediate representations” –Present real ontologies to be relevant to needs “Views” Scalable simplicity for end-users requires sophisticated architecture –“Swans paddle furiously under water” Decoupled distributed environment –“Owned” by the domain experts

16 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 –Information capture –Privacy, confidentiality, & consent –Information integration – sharing of meaning Common “Ontologies” are a key resource

17 CLEF Consortium 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