1. iMinds DepartmentMEDICAL ITHealthData AnalyticsandDecision SupportProf.Dr. Bart De MoorBart.DeMoor@iminds.be

2. Department Med ITTrendsP3 x P4 medicineDecision supportcases 3. MEDICAL IT DEPARTMENT200papers / year10PhDs /year10PI H-index > 254 Nature papers8Patents207PhD students21PIs9RM8Health spinoffs 4. Department Med ITTrendsP3 x P4 medicineDecision supportcases 5. DEMOGRAPHY & COSTSagesurvival & spending 6. TECHNOLOGY FOREVOLVING NEEDS 7. Moores law:computing powerdoublesevery 18 monthsNext GenerationSequencingCarlsons law:complexity/costevolvesexponentially 8. WWW 9. 1 million = 1 000 0001 billion = 1 000 000 0001 trillion = 1 000 000 000 0001 quadrillion =1 000 000 000 000 0001 kB = 1 0001 MB = 1 000 0001 GB = 1 000 000 0001 TB = 1 000 000 000 0001 PB = 1 000 000 000 000 0001 TB= large university library= 212 DVD discs= 1430 CDs= 3 year music in CD quality 10. GENOMEDATAGS-FLX RocheApplied Science 454Sequencers10 Human genome project(2003) 13 year project $300 million valuewith 2002 technology Personal genome (2007) Genome of JamesWatson, 2 months $1 000 000 1000-genome1,00E+111,00E+101,00E+091,00E+081,00E+071,00E+061,00E+051,00E+041,00E+031,00E+021,00E+011,00E+001,00E-011,00E-021,00E-031,00E-041,00E-051,00E-06 Expected 2012-2020 1,00E-071990 1995 2000 2002 2005 2007 2010 2015Cost per base pairGenome cost 11. TSUNAMI OF MEDICAL DATAPACSUZ Leuven1,6 PetaByteGenomics coreHiSeq 2000 fullspeed exomesequencing1 TeraByte / week1 smallanimalimage1GigaByte1 CD-ROM750MegaBytesequencing all newbornsby 2020 (125k births /year)125 PetaByte / yearindex of 20millionBiomedicalPubMedrecords23 GigaByte1 slice mousebrain MSI at10 mresolution81 GigaByteraw NGS dataof 1 full genome1 TeraByte 12. 3P x 4PMEDICINE 13. DATA-DRIVEN 3PMEDICINEPROFESSIONALS: Clinicians, Researchers, PATIENTS: Empowerment, Associations, POLICY MAKERS: Hospitals, Health Insurance, Social Security, 14. DATA-DRIVEN 4PMEDICINEPERSONALIZED "customized" diagnosis and treatmentPREVENTIVE better than curationPREDICTIVE determine risk profiles & predict outcomePARTICIPATORY involve the patient 15. Department Med ITTrendsP3 x P4 medicineDecision supportcases 16. PARTICIPATORYIOTA app:populationbasedassessmentof ovariantumour malignancy:LeuvenMalmLundLublinGenk# patients: 1066 + 1938MonzaLondonMaurepas ParisMilanRomeNapelsOntario, CanadaBolognaSardiniaBeijing, ChinaIOTA app available in iTunes app store and on http://homes.esat.kuleuven.be/~sistawww/biomed/iota/ 17. PERFORMANCEPerformance of an expert Performance ofIOTA modelsPerformance ofold modelsPerformance ofnon-expertsYou share, we care ! 18. PREDICTIVE 19. PERSONALIZEDLOGIC-InsulinwithTight Glycemic Control in intensive care lowers mortalityControl algorithm LOGIC-Insulin (automated customized patient pilot)400 clinical trials 20. PREVENTIVEACACATTAAATCTTATATGCTAAAACTAGGTCTCGTTTTAGGGATGTTTATAACCATCTTTGAGATTATTGATGCATGGTTATTGGTTAGAAAAAATATACGCTTGTTTTTCTTTCCTAGGTTGATTGACTCATACATGTGTTTCATTGAGGAAGGAACTTAACAAAACTGCACTTTTTTCAACGTCACAGCTACTTTAAAAGTGATCAAAGTATATCAAGAAAGCTTAATATAAAGACATTTGTTTCAAGGTTTCGTAAGTGCACAATATCAAGAAGACAAAAATGACTAATTTTGTTTTCAGGAAGCATATATATTACACGAACACAAATCTATTTTTGTAATCAACACCGACCATGGTTCGATTACACACATTAAATCTTATATGCTAAAACTAGGTCTCGTTTTAGGGATGTTTATAACCATCTTTGAGATTATTGATGCATGGTTATTGGTTAGAAAAAATATACGCTTGTTTTTCTTTCCTAGGTTGATTGAACACATTAAATCTTATATGCTAAAACTAGGTCTCGTTTTAGGGATGTTTATAACCATCTTTGAGATTATTGATGCATGGTTATTGGTTAGAAAAAATATACGCTTGTTTTTCTTTCCTAGGTTGATTGACTCATACATGTGTTTCATTGAGGAAGGAACTTAACAAAACTGCACTTTTTTCAACGTCACAGCTACTTTAAAAGTGATCAAAGTATATCAAGAAAGCTTAATATAAAGACATTTGTTTCAAGGTTTCGTAAGTGCACAATATCAAGAAGACAAAAATGACTAATTTTGTTTTCAGGAAGCATATATATTACACGAACACAAATCTATTTTTGTAATCAACACCGACCATGGTTCGATTACACACATTAAATCTTATATGCTAAAACTAGGTCTCGTTTTAGGGATGTTTATAACCATCTTTGAGATTATTGATGCATGGTTATTGGTTAGAAAAAATATACGCTTGTTTTTCTTTCCTAGGTTGATTGAACACATTAAATCTTATATGCTAAAACTAGGTCTCGTTTTAGGGATGTTTATAACCATCTTTGAGATTATTGATGCATGGTTATTGGTTAGAAAAAATATACGCTTGTTTTTCTTTCCTAGGTTGATTGACTCATACATGTGTTTCATTGAGGAAGGAACTTAACAAAACTGCACTTTTTTCAACGTCACAGCTACTTTAAAAGTGATCAAAGTATATCAAGAAAGCTTAATATAAAGACATTTGTTTCAAGGTTTCGTAAGTGCACAATATCAAGAAGACAAAAATGACTAATTTTGTTTTCAGGAAGCATATATATTACACGAACACAAATCTATTTTTGTAATCAACACCGACCATGGTTCGATTACACACATTAAATCTTATATGCTAAAACTAGGTCTCGTTTTAGGGATGTTTATAACCATCTTTGAGATTATTGATGCATGGTTATTGGTTAGAAAAAATATACGCTTGTTTTTCTTTCCTAGGTTGATTGA 21. GENOMIC DATA FUSIONHigh-throughputgenomicsData analysis Candidate genesInformation sourcesCandidate prioritizationValidationEndeavour: Aerts et al., Nature Biotechnology, 2006 22. PROFESSIONALgenomic data fusion:trace disease-causing variants20x more accurateBron:De Tijd, woensdag 23 oktober 2013Sifrim, Popovic et al,Nature Methods, 2013http://homes.esat.kuleuven.be/~bioiuser/eXtasy/ 23. PREDICTIVEGenetic BiomarkersFor Leukemia Armstrong SA et al. Nat Genet. 2002 Jan;30(1):41-7.12 600 genes72 patients- 28 Acute Lymphoblastic Leukemia (ALL)- 24 Acute Myeloid Leukemia (AML)- 20 Mixed Linkage Leukemia (MLL) 24. E D. Green et al. Nature 470, 204-213 (2011)doi:10.1038/nature09764 25. POLICYSocial security data miningfor evidence-based policy decision making Mining @ CM to detect diabetes risk from billing data Model & visualize current health care mechanisms (resources,consumption, outliers, ) Deduce optimal policy changes & best practices (in e.g. prescriptionbehaviour) Mine RIZIV ? ! 26. iMindsMEDICAL ITwww.iminds.be/medicalitTrendsP3 x P4 medicineDecision supportcases