Exploring the Integration of Health and Bioinformatics for Improved Patient Care

1. Joined up Health and Bio Informatics: Alan RectorBio and Health Informatics Forum/Medical Informatics GroupDepartment of Computer ScienceUniversity of Manchester rector@cs.man.ac.ukwww.cs.man.ac.uk/mig img.man.ac.ukwww.clinical-escience.orgmygrid.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. Need more and better clinical information • Which scales • In Size • In Complexity A Convergence of Need • Post genomic research • Safe, high quality, evidence based health care Knowledge is Fractal

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

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: CLEFJoining up Health Care & Bioscience in Cancer • Clinicale-Science Framework • Clinical care • Clinical research • Clinical bioscience • Genotype meets Phenotype • New technologies for healthcare • A focus to adapt new technologies to healthcare • New ways to do clinical research • Faster, safer, easier, better • Trial design, execution, archiving, reporting

9. CLEFTowards and “end-to-end” solutionin 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 isanatomicallynormal” OpenGALEN & OWL

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

14. Avoiding combinatorial explosions • The “Exploding Bicycle”From “phrase book” to “dictionary + grammar” • 1980 - ICD-9 (E826) 8 • 1990 - READ-2 (T30..) 81 • 1995 - READ-3 87 • 1996 - 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 Consortiumwww.clinical-escience.org • 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