How can we build a strong and thriving open source / open data community around FAIR principles?

Transcription

1 How can we build a strong and thriving open source / open data community around FAIR principles? PERSONAL HEALTH TRAIN WORKSHOP, UTRECHT, NOVEMBER 10, 2016 Kees van Bochove, CEO & Founder, The Hyve

2 Agenda u Introduction u Flash updates from The Hyve 4 example health trains u TraIT/BBMRI: mapping clinical studies into transmart u cbioportal: mapping cancer genomics data into cbioportal u EMIF: mapping 50 million EU patients into OMOP u RADAR: import wearable sensor data into hospital patient records u Future directions for FAIR infrastructure & community 2

3 The Hyve u Professional support for open source so*ware for bioinforma1cs and transla1onal research so5ware, such as transmart, cbioportal, i2b2, Galaxy, ADAM and OHDSI Core values Share Reuse Specialize Office Loca5ons Utrecht, Netherlands Cambridge, MA, United States Services So5ware development Data science services Consultancy Hos1ng / SLAs Mission Enable pre-compe11ve collabora1on in life science R&D by leveraging open source so*ware Fast-growing Started in people by now 3

4 Open Source in Precision Medicine Clinical / Healthcare: Datawarehousing: Data visualisation: Imaging: Biobanking: Workflow / NGS: Scientific compute: Data management / Study design: 4

5 Interdisciplinary team so5ware engineers, data scien1sts, project managers & staff; exper1se in bioinforma1cs, medical informa1cs, so5ware engineering, biosta1s1cs etc. 5

6 6 Offices at the Arthur van Schendelstraat in Utrecht

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8 8 PI: Prof. Gerrit Meijer, Netherlands Cancer Institute

9 TranSMART Platform: Scientific Function DATA CLINICAL GENETICS SENSORS IMAGING BIOLOGY MEDICINE UNDER STANDING 9

10 Contributors

11 TraIT data workflow Hospital (IT) HIS PACS LIS Samples (IT) BIMS P seu d o n y m iz a ti o n data domains clinical data OpenClinica imaging data NBIA + AIM biobanking CBM-NL Translational Research (IT) integrated data transmart/i2b2 datawarehouse translational analytics workbench transmart/ cohort explorer R Public Data experimental data e.g. Galaxy, Chipster e.g. PhenotypeDB, Annai Systems Galaxy

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13 TranSMART: working with clinical data

14 2. TRANSLATIONAL RESEARCH DATA 14

15 cbioportal for Cancer Genomics current community a.o. 15

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17 3. POPULATION HEALTH DATA 17

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20 OMOP Common Data Model v5.0 v OMOP = Observational Medical Outcomes Partnership v CDM = Common Data Model v SQL Tables 20

21 Overview of ontologies used in OMOP over 80 healthcare vocabularies mapped 21

22 ATLAS: Individual Patient Profile 22

23 EMIF-Platform Available data sources in EMIF EMIF-Available Data Sources; EXAMPLES Status Jan 2016 >40 million Approximate total (cumulative) number of subjects THIN 12M PHARMO 10M SIDIAP 6M ARS 3.6M IPCI 2.8M AUH 2.3M HSD 1.6M IMASIS 1M SCTS 475K PEDIANET 400K EGCUT 52K SDR 2K MAAS 1K 100 1,000 10, ,000 1,000,000 10,000, ,000,000 23

24 24 PI: Prof. Matthew Hotopf, King s College London

25 RADAR-CNS: Focus areas from diagnose & treat! predict & pre-empt u Epilepsy u Monitoring and predicting epileptic seizures u Multiple Sclerosis u Monitoring exacerbations and disease state u Depression u Monitoring for possible relapses, plan timely interventions u Predict bipolar state transitions 25

26 Continuous Patient Assessment 26

27 Preliminary Technology Stack Mobile data streams Direct data streams Cloud data endpoints Analytics - Feedback to mobile apps - Streaming analytics - Translational analytics 27

28 Software development RADAR platform Freiburg Epilepsy Study King s Epilepsy Study Gathering requirements & steering platform development through supporting actual studies Developing Core RADAR-CNS Platform 28

29 Conclusion u What s the core in FAIR? u Digital (data) interoperability standards u Use existing open standards which are adopted: e.g. the OMOP data model u Aspects of the FAIR community u Software community: DTL (wiki etc.) for now, but need to expand u Data community: positioning via e.g. ELIXIR u Science community: contribute to scientific conferences 29

30 Training is needed for: u Research scientists & labs generating data Where is the most urgent need? u u u u u u u u u Care providers that are required to share data to their patients Medical informatics researchers looking for data Patients looking for sound medical advice Patients that want to share their data with friends & family Citizens looking for nutritional / behaviour advice Citizens looking for data to do citizen science Physicians need access to medical records (including patient-recorded outcomes) of their patients Epidemiologists & medical outcome researchers at pharma companies looking for real world data 30

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