Superminds_2014_medicalIT

iMinds DepartmentMEDICAL IT

Health Data Analytics and Decision Support

Prof.Dr. Bart De Moor

Bart.DeMoor@iminds.be

22/10/14

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Department Med IT

Trends

P3 x P4 medicine

Decision support cases

22/10/14

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MEDICAL IT DEPARTMENT

200papers / year10PhDs / year

10PI H-index > 25

4 Nature papers

8 Patents

207PhD students

21PIs

9RM

8Health spinoffs

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Department Med IT

Trends

P3 x P4 medicine

Decision support cases

22/10/14

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Demography & costs

agesurvival & spending

Technology FOR evolving needs

Moores law:computing powerdoublesevery 18 months

Next GenerationSequencingCarlsons law:complexity/costevolvesexponentially

source: webgaga.fr

By 2030, 25% of the population will be over 65. This will bring along a huge increase in age related and chronic diseases such as diabetes, heart failure, cancer and neuro-degenerative diseases such as Alzheimers.

These already pose a large burden on the health care budget, and it will only worsen if nothing is changed. There will be an enormous challenge to provide qualitative health care at a sustainable cost for society.

But the patient himself is already one of the important factors for this necesarry change in the system.

source: www.kiesbeter.nl

The patient of tomorrow is not the same as the one of today, and we already see this change. This screenshot for instance is from a Dutch website kiesbeter.nl. Its a clear example of enabling patient empowerment.

And there are also important technological trends that can help tackle these challenges.

source: http://arstechnica.com/business/2012/05/bandwidth-explosion-as-internet-use-soars-can-bottlenecks-be-averted/

Bandwith increasing, but not sufficient for moving around big dataBroadband capacity is increasing, we (and our stuff) is always connected, think about open and linked data, internet of things, google, facebook

source: http://claretownhill.typepad.com/weblog/2009/06/counting-down-to-the-end-of-moores-law.html

The same holds true for computing power, which is described by Moores law: the number of transistors on an Integrated Circuit doubles every 2 years.Also think of supercomputing and cloud computing.

But being connected and having access to computing power dont mean anything if no-one would be contributing to the system. This is where something that is also called the quantified self comes into the picture. We have nowadays so many options to our disposal to generate a unique picture of every individual from the molecular to the macro level.

www

1 million = 1 000 0001 billion = 1 000 000 0001 trillion = 1 000 000 000 0001 quadrillion = 1 000 000 000 000 0001 kB = 1 000 1 MB = 1 000 0001 GB = 1 000 000 0001 TB = 1 000 000 000 0001 PB = 1 000 000 000 000 000 1 TB = large university library= 212 DVD discs = 1430 CDs= 3 year music in CD quality

Genome data10Human genome project (2003)13 year project$300 million value with 2002 technologyPersonal genome (2007)Genome of James Watson, 2 months$1 000 0001000-genomeExpected 2012-2020

GS-FLX Roche Applied Science 454Sequencers

source: tulane.edu

Biomedical measurements generate a unique picture of every individual from the molecular to the macro level.

And all these measurements and numbers together tell the tale of our unique health status.

Through telemonitoring, brain and heart signals can be measured even from a distance. Portable diagnostic devices can keep track of our blood sugar levels, or any other blood component that that might tell us something about our health status.

Besides that, the hospital of today has access to lots of different imaging devices.

And also on the molecular level think of your DNA or the proteins that make your body function - we have made immense progress. Comparable to Moores law for computing performance, DNA sequencing has its own law of Carlson that states that sequencing the 3 billion basepairs of our genome is becoming cheaper and cheaper.

in biobanks, samples from biopsies, blood and DNA tests are stored. When patient characteristics can be linked with treatments and clinical outcomes, we can develop truly personalized medicine

And finally, theres all the information that we share about our health, or the way we experience certain treaments. Remember this patient empowerment I talked about.

When we can make use all of this information to our advantage, imagine where that can take us.

Tsunami of medical dataPACSUZ Leuven

1,6 PetaByte

Genomics core HiSeq 2000 full speed exome sequencing

1 TeraByte / week

1 small animal image

1 GigaByte1 CD-ROM

750 MegaByte

sequencing all newborns by 2020 (125k births / year)

125 PetaByte / yearindex of 20 million Biomedical PubMed records

23 GigaByte1 slice mouse brain MSI at 10 m resolution

81 GigaByteraw NGS data of 1 full genome

1 TeraByte

source: tulane.edu

Biomedical measurements generate a unique picture of every individual from the molecular to the macro level.

And all these measurements and numbers together tell the tale of our unique health status.

Through telemonitoring, brain and heart signals can be measured even from a distance. Portable diagnostic devices can keep track of our blood sugar levels, or any other blood component that that might tell us something about our health status.

Besides that, the hospital of today has access to lots of different imaging devices.

And also on the molecular level think of your DNA or the proteins that make your body function - we have made immense progress. Comparable to Moores law for computing performance, DNA sequencing has its own law of Carlson that states that sequencing the 3 billion basepairs of our genome is becoming cheaper and cheaper.

in biobanks, samples from biopsies, blood and DNA tests are stored. When patient characteristics can be linked with treatments and clinical outcomes, we can develop truly personalized medicine

And finally, theres all the information that we share about our health, or the way we experience certain treaments. Remember this patient empowerment I talked about.

When we can make use all of this information to our advantage, imagine where that can take us.

3P x 4PMEDICINE

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Data-driven 3P MEDICINEPROFESSIONALS: Clinicians, Researchers, PATIENTS: Empowerment, Associations, POLICY MAKERS: Hospitals, Health Insurance, Social Security,

Personalized: Preventive: prevention is always better than curation, tailored to specific patientsPredictive: precise predictions with modern technology, determine risk profiles, predict progression and outcomeParticipatory: correct and complete information to participate in the decision process

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Data-driven 4P MEDICINEPERSONALIZED "customized" diagnosis and treatment PREVENTIVE better than curationPREDICTIVE determine risk profiles & predict outcomePARTICIPATORY involve the patient

Personalized: Preventive: prevention is always better than curation, tailored to specific patientsPredictive: precise predictions with modern technology, determine risk profiles, predict progression and outcomeParticipatory: correct and complete information to participate in the decision process

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Department Med IT

Trends

P3 x P4 medicine

Decision support cases

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ParticipatoryIOTA app:population based assessment of ovariantumour malignancy:IOTA app available in iTunes app store and on http://homes.esat.kuleuven.be/~sistawww/biomed/iota/

LeuvenMalmMonzaLondonMaurepasParisRomeNapelsMilan# patients: 1066 + 1938 LundLublinGenk

Ontario, CanadaBolognaSardinia

Beijing, China

andere voorbeelden:IOTA app - predictive models (IWT-TBM stadius),drug target prioritisation (phd project + O&O stadius)Sublima (FP7, development human PET-MRI ibitech)Motion Correction for Chest Tomosynthesis, IWT Baekeland, 2012-2016. visielabNeoGuard (IWT TBM + spinoff idee stadius)

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Performance

Performance of an expert

Performance of IOTA models

Performance of an expertPerformance of old models

Performance of non-expertsYou share, we care !

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PreDICTIVE

PersonalizedLOGIC-InsulinTight Glycemic Control in intensive care lowers mortality Control algorithm LOGIC-Insulin (automated customized patient pilot)400 clinical trials

with

NeoGuard = Automated assessment of neonatal brain functioning It has many advantages with respect to current practice:-The expert time to interpret the numerous hours of cEEG will be significantly reduced. In just 1-click, an overview of EEG activities, detections of neonatal seizures and the improvement/deterioration of brain functioning will be shown. -Pathological patterns (i.e. neonatal epileptic seizures) will be more accurately detected, leading to improved diagnosis, and consequently better treatment and follow-up. -Insight into the neurodevelopmental outcome will be more objective. In addition, a universal system for monitoring will enable comparisons between several centres and improved future treatments. -Small and peripheral NICUs would get access to expert knowledge, allowing faster treatment and transfer to larger centres if necessary. -Non-expert clinician staff and nurses will get insight into neonatal brain functions (e.g. while transporting ill neonates and performing ambulatory EEG monitoring).

LOGIC-Insulin:10 mio adult ICU patients / year (EU + US) (1-2 b$ market)Tight Glycemic Control (TGC) in intensive care unit lowers mortalityimplement through LOGIC-Insulin: semi-automatic control system that advises nurse on insulin dosage and blood sampling interval aiming at TGC and avoiding hypoglycemiaLOGIC-I randomized clinical trial (single-centre): compared with expert nurses, LOGIC-Insulin showed improved efficacy of TGC without increasing rate of hypoglycemiaLOGIC-II randomized clinical trial (multi-centre): Start February 2014

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Preventive

ACACATTAAATCTTATATGCTAAAACTAGGTCTCGTTTTAGGGATGTTTATAACCATCTTTGAGATTATTGATGCATGGTTATTGGTTAGAAAAAATATACGCTTGTTTTTCTTTCCTAGGTTGATTGACTCATACATGTGTTTCATTGAGGAAGGAACTTAACAAAACTGCACTTTTTTCAACGTCACAGCTACTTTAAAAGTGATCAAAGTATATCAAGAAAGCTTAATATAAAGACATTTGTTTCAAGGTTTCGTAAGTGCACAATATCAAGAAGACAAAAATGACTAATTTTGTTTTCAGGAAGCATATATATTACACGAACACAAATCTATTTTTGTAATCAACACCGACCATGGTTCGATTACACACATTAAATCTTATATGCTAAAACTAGGTCTCGTTTTAGGGATGTTTATAACCATCTTTGAGATTATTGATGCATGGTTATTGGTTAGAAAAAATATACGCTTGTTTTTCTTTCCTAGGTTGATTGAACACATTAAATCTTATATGCTAAAACTAGGTCTCGTTTTAGGGATGTTTATAACCATCTTTGAGATTATTGATGCATGGTTATTGGTTAGAAAAAATATACGCTTGTTTTTCTTTCCTAGGTTGATTGACTCATACATGTGTTTCATTGAGGAAGGAACTTAACAAAACTGCACTTTTTTCAACGTCACAGCTACTTTAAAAGTGATCAAAGTATATCAAGAAAGCTTAATATAAAGACATTTGTTTCAAGGTTTCGTAAGTGCACAATATCAAGAAGACAAAAATGACTAATTTTGTTTTCAGGAAGCATATATATTACACGAACACAAATCTATTTTTGTAATCAACACCGACCATGGTTCGATTACACACATTAAATCTTATATGCTAAAACTAGGTCTCGTTTTAGGGATGTTTATAACCATCTTTGAGATTATTGATGCATGGTTATTGGTTAGAAAAAATATACGCTTGTTTTTCTTTCCTAGGTTGATTGAACACATTAAATCTTATATGCTAAAACTAGGTCTCGTTTTAGGGATGTTTATAACCATCTTTGAGATTATTGATGCATGGTTATTGGTTAGAAAAAATATACGCTTGTTTTTCTTTCCTAGGTTGATTGACTCATACATGTGTTTCATTGAGGAAGGAACTTAACAAAACTGCACTTTTTTCAACGTCACAGCTACTTTAAAAGTGATCAAAGTATATCAAGAAAGCTTAATATAAAGACATTTGTTTCAAGGTTTCGTAAGTGCACAATATCAAGAAGACAAAAATGACTAATTTTGTTTTCAGGAAGCATATATATTACACGAACACAAATCTATTTTTGTAATCAACACCGACCATGGTTCGATTACACACATTAAATCTTATATGCTAAAACTAGGTCTCGTTTTAGGGATGTTTATAACCATCTTTGAGATTATTGATGCATGGTTATTGGTTAGAAAAAATATACGCTTGTTTTTCTTTCCTAGGTTGATTGA

genomic data fusion

High-throughputgenomics

Data analysisCandidate genes

Information sources

Candidate prioritizationValidationEndeavour: Aerts et al., Nature Biotechnology, 2006

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PROFESSIONAL genomic data fusion:trace disease-causing variants20x more accurate

Bron:De Tijd, woensdag 23 oktober 2013

Sifrim, Popovic et al, Nature Methods, 2013http://homes.esat.kuleuven.be/~bioiuser/eXtasy/

andere voorbeelden:IOTA app - predictive models (IWT-TBM stadius),drug target prioritisation (phd project + O&O stadius)Sublima (FP7, development human PET-MRI ibitech)Motion Correction for Chest Tomosynthesis, IWT Baekeland, 2012-2016. visielabNeoGuard (IWT TBM + spinoff idee stadius)

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PrEDICTIVE

Armstrong SA et al. Nat Genet. 2002 Jan;30(1):41-7. 12 600 genes 72 patients- 28 Acute Lymphoblastic Leukemia (ALL)- 24 Acute Myeloid Leukemia (AML)- 20 Mixed Linkage Leukemia (MLL)

Genetic BiomarkersFor Leukemia

E D. Green et al. Nature 470, 204-213 (2011) doi:10.1038/nature09764

policy

Social security data mining for evidence-based policy decision making

Mining @ CM to detect diabetes risk from billing dataModel & visualize current health care mechanisms (resources, consumption, outliers, )Deduce optimal policy changes & best practices (in e.g. prescription behaviour)Mine RIZIV ? !

iMindsMEDICAL IT

www.iminds.be/medicalit

Trends

P3 x P4 medicine

Decision support cases

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Black slides can best be inserted instead of using B short code because presentations will be given using a remote control.22/10/14

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Chart11030000000000130000000000.26000000000.092700000000.03900000000.0003333339999990.00000333339999.990.0000001300

Cost per base pairGenome cost

Sheet1YearCost per base pairGenome cost1990103000000000019951300000000020000.260000000020020.0927000000020050.039000000020070.00033333399999920100.00000333339999.9920150.0000001300

Sheet10000000000000000

Cost per base pairGenome cost

Sheet2

Sheet3