REPRODUCING INSIGHT CONDITIONS FOR A BIGGER SCALE OF HEALTH SCIENCE

Transcription

1 REPRODUCING INSIGHT CONDITIONS FOR A BIGGER SCALE OF HEALTH SCIENCE Iain Buchan Director, Farr HeRC Director, Centre for Health Informatics, University of Manchester 1 st April 2015 Reproducibility and Reliability of Biomedical Research Symposium Wellcome Trust (with AMS, MRC, BBSRC), London

2 Big Data to Big Scale of Insights DATA METHODS & OUTPUTS EXPERTISE Vast volume, velocity, variety TSUNAMI Supra-linear growth in papers & tools BLIZZARD Similar number of analysts DROUGHT Scaling Medical Research(ers) 1. Assist hypothesis formation with data 2. Employ heterogeneous populations/settings/insights 3. Enable automatic replication and updating of findings

3 Hypothesising with Data switch group Sensitization Group 1,053 Children 8 Allergens P(Sens n) in year 1 Sensitized Age 1 Sensitized Age 3 Sensitized Age 5 Sensitized Age 8 P(Gain) P (Loose) Sens n 3 intervals Sens n state Skin Test Age 1 Blood Test Age 1 Skin Test Age 3 Blood Test Age 3 Skin Test Age 5 Blood Test Age 5 Skin Test Age 8 Blood Test Age 8 Mite Cat Dog Pollen Egg Milk Mold Peanut P(+ skin) Sens P(+ blood) Sens Machine-learning software & partial statistical models P(+ skin) Not Sens P(+ blood) Not Sens

4 New Asthma Risk Factor Found Allergic sensitisation patterns learned from data

5 Scaling-up: STELAR Consortium of birth cohort studies, methodologists, and basic scientists (incl. MRC Systems Immunology) M a n c h e s t e r A s t h n m a a d A l l e r g y S t u y d

6 Similar Clinical E-Lab Requirements Commons of Metadata and Information Governance (Clinical & Research) Research Safe Haven Clinical Care Lab 1 Research Objects Lab 2 De-identified Records Encrypted (SHA1 & AES256); Certified (ISO 27001) Identified Records Lab 3 Lab 4 Study Protocol /Assessment Study Recruit RAPID REPLICATION Research protocol Codes for the data Statistical scripts Results in progress Draft manuscript Slides etc. Researcher Linkable Data Providers Clinician Patient

7 Asthma E-Lab Data & Harmonized Metadata from Cohorts MAAS SEATON ASHFORD STELAR elab STELAR Researchers Data Extracts Modelling Networking: Ideas, Activities, Results, Meanings ALSPAC IOW COLLABORATIVE MODELLING TIMELY, INVOLVING & EVOLVING RESEARCH

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9 Learned Atopy Classes Portable Lazic et al, Allergy 2013; 68(6):

10 Risk Factor Development Risk group induced from data shows different natural history of airways resistance Belgrave et al, Am J Respir Crit Care Med 2014;189(9):1101-9

11 Received Wisdom: Atopic March Progression of allergy Eczema Asthma Rhinitis From: Spergel & Paller, 2003 Inferred from population summary Assumed causal link between eczema asthma & rhinitis From: World Allergy Organization, 2014 Clinical response: target children with eczema to reduce progression to asthma

12 Individual-level Longitudinal Analysis Eczema Class Eczema Age 1 Eczema Age 3 Eczema Age 5 Eczema Age 8 Eczema Age 11 Wheeze Class Wheeze Age 1 Wheeze Age 3 Wheeze Age 5 Wheeze Age 8 Wheeze Age 11 Rhinitis Class Rhinitis Age 1 Rhinitis Age 3 Rhinitis Age 5 Rhinitis Age 8 Rhinitis Age 11 Children (n=9801) Latent Class Disease Profile

13 Myth Bust by Learning from Data No Disease (n=5023) 51.2% Atopic March (n=302) 3.1% Persistent Eczema and Wheeze (n=266) 2.7% Persistent Eczema with Later-onset Rhinitis (n=456) 4.7% Age (Years) Age (Years) Age (Years) Age (Years) MRC STELAR consortium working at scale across MAAS and ALSPACS cohorts Persistent Wheeze with Later-onset Rhinitis (n=556) 5.7% Transient Wheeze (n=755) 7.7% Eczema Only (n=1506) 15.3% Rhinitis Only (n=936) 9.6% Age (Years) Age (Years) Age (Years) Age (Years) Wheeze Eczema Rhinitis From: Belgrave et al. Developmental Profiles of Eczema, Wheeze, and Rhinitis: Two Population-Based Birth Cohort Studies. PlosMedicine 2014

14 Reproducible Bio-Health Insight Biological Mechanisms Given Phenotype Modelled Phenotype Clinical Outcomes Bioinformatics Health Informatics Integrative Informatics, Modelling and Insight is Needed

15 Clinical Outcome Prediction EuroScore Production line of clinical prediction models is broken Typical calibration drift EU Directive 2007/47: The law now sees algorithms as medical devices From G. Hickey & B. Bridgewater

16 Patient / Mobile Data Shared Care Record Molecular characterisation Core Outcomes Dynamic Prediction Models Clinical Performance Networks Routine Randomisation Learning from Other Systems Depth, Breadth, Trust and Scale From data castles Learning Health System Insight Node To large scale research over shared resources, as in Physics INTEGRATIVE INFORMATICS