1O p en G A L E N

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

Alan RectorAlan Rector

Bio and Health Informatics Forum/Bio and Health Informatics Forum/Medical Informatics GroupMedical Informatics Group

Department of Computer ScienceDepartment of Computer ScienceUniversity of ManchesterUniversity of Manchester

rector@cs.man.ac.ukrector@cs.man.ac.ukwww.cs.man.ac.uk/mig img.man.ac.ukwww.cs.man.ac.uk/mig img.man.ac.uk

www.clinical-escience.orgwww.clinical-escience.orgmygrid.man.ac.ukmygrid.man.ac.uk

2O p en G A L E N

The ProblemThe 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

3O p en G A L E N

A Convergence of NeedA 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

4O p en G A L E N

A convergence of TechnologiesA 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

5O p en G A L E N

A Unique TimeA 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!

6O p en G A L E N

Protocol/Collection-based Protocol/Collection-based researchresearch

Results in vivo

Research idea

Protocol Authoring

ToolsData

Collection Tools

Shared CollectionsModels & Standards

Protocol Approval

ToolsAutomatic

Patient Screening

Data Analysis Tools

Plausibilityin

Silico/Collecto

7O p en G A L E N

““Stones in the Road”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

8O p en G A L E N

One Response:One Response: CLEF CLEFJoining up Health Care & Bioscience in Joining up Health Care & Bioscience in

Cancer Cancer

• ClinicalClinical e-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

9O p en G A L E N

CLEFCLEFTowards and “end-to-end” solutionTowards and “end-to-end” solution

in an ethical frameworkin an ethical framework

• Patient care

• Formulation of clinical studies

• Information capture

• Information representation

• Information analysis and integration

• Knowledge & hypothesis generation

• Clinical support

10O p en G A L E N

CLEF: CLEF: A meeting of open A meeting of open technologiestechnologies

• 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

11O p en G A L E N

CLEF: Language TechnologyCLEF: 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

12O p en G A L E N

CLEF Logic-based Ontologies: CLEF Logic-based Ontologies: Conceptual LegoConceptual Lego

“SNPolymorphism of CFTRGene causing Defect in MembraneTransport of ChlorideIon causing Increase in Viscosity of Mucus in CysticFibrosis…”

“Hand which isanatomicallynormal”

OpenGALEN & OWL

13O p en G A L E N

Bridging Scales Bridging Scales with Ontologieswith Ontologies

GenesSpecies

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

14O p en G A L E N

Avoiding combinatorial explosionsAvoiding 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

15O p en G A L E N

Making it simple: ToolsMaking 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

16O p en G A L E N

SummarySummary

• 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

17O p en G A L E N

CLEF ConsortiumCLEF Consortiumwww.clinical-escience.orgwww.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