This document provides an overview of the iMinds Medical IT Department. It summarizes that the department conducts research in areas related to data analytics and decision support for health, with a focus on trends in personalized (P3) and predictive (P4) medicine. The department publishes over 200 papers per year, has over 200 PhD students, and has helped launch 8 health-related spinoff companies. It is involved in projects related to analyzing genomic and medical imaging data, developing decision support systems, and mining data to help inform healthcare policymaking.

1. iMinds Department
MEDICAL IT
Health
Data Analytics
and
Decision Support
Prof.Dr. Bart De Moor
Bart.DeMoor@iminds.be

2. Department Med IT
Trends
P3 x P4 medicine
Decision support
cases

3. MEDICAL IT DEPARTMENT
200
papers / year
10
PhDs /
year
10
PI H-index > 25
4 Nature papers
8
Patents
207
PhD students
21
PIs
9
RM
8
Health spinoffs

4. Department Med IT
Trends
P3 x P4 medicine
Decision support
cases

5. DEMOGRAPHY & COSTS
age
survival & spending

6. TECHNOLOGY FOR
EVOLVING NEEDS

7. Moore’s law:
computing power
doubles
every 18 months
Next Generation
Sequencing
Carlson’s law:
complexity/cost
evolves
exponentially

8. WWW

9. 1 million = 1 000 000
1 billion = 1 000 000 000
1 trillion = 1 000 000 000 000
1 quadrillion =
1 000 000 000 000 000
1 kB = 1 000
1 MB = 1 000 000
1 GB = 1 000 000 000
1 TB = 1 000 000 000 000
1 PB = 1 000 000 000 000 000
1 TB
= large university library
= 212 DVD discs
= 1430 CDs
= 3 year music in CD quality

10. GENOME
DATA
GS-FLX Roche
Applied Science 454
Sequencers
10
• Human genome project
(2003)
– 13 year project
– $300 million value
with 2002 technology
• Personal genome (2007)
– Genome of James
Watson, 2 months
– $1 000 000
• €1000-genome
1,00E+11
1,00E+10
1,00E+09
1,00E+08
1,00E+07
1,00E+06
1,00E+05
1,00E+04
1,00E+03
1,00E+02
1,00E+01
1,00E+00
1,00E-01
1,00E-02
1,00E-03
1,00E-04
1,00E-05
1,00E-06
– Expected 2012-2020 1,00E-07
1990 1995 2000 2002 2005 2007 2010 2015
Cost per base pair
Genome cost

11. TSUNAMI OF MEDICAL DATA
PACS
UZ Leuven
1,6 PetaByte
Genomics core
HiSeq 2000 full
speed exome
sequencing
1 TeraByte / week
1 small
animal
image
1
GigaByte
1 CD-ROM
750
MegaByte
sequencing all newborns
by 2020 (125k births /
year)
125 PetaByte / year
index of 20
million
Biomedical
PubMed
records
23 GigaByte
1 slice mouse
brain MSI at
10 μm
resolution
81 GigaByte
raw NGS data
of 1 full genome
1 TeraByte

12. 3P x 4P
MEDICINE

13. DATA-DRIVEN 3P
MEDICINE
PROFESSIONALS: Clinicians, Researchers, …
PATIENTS: Empowerment, Associations, …
POLICY MAKERS: Hospitals, Health Insurance, Social Security, …

15. DATA-DRIVEN 4P
MEDICINE
PERSONALIZED "customized" diagnosis and treatment
PREVENTIVE better than curation
PREDICTIVE determine risk profiles & predict outcome
PARTICIPATORY involve the patient

16. Department Med IT
Trends
P3 x P4 medicine
Decision support
cases

17. PARTICIPATORY
IOTA app:
population
based
assessment
of ovarian
tumour malignancy:
Leuven
Malmö
Lund
Lublin
Genk
# patients: 1066 + 1938
Monza
London
Maurepas Paris
Milan
Rome
Napels
Ontario, Canada
Bologna
Sardinia
Beijing, China
IOTA app available in iTunes app store and on http://homes.esat.kuleuven.be/~sistawww/biomed/iota/

18. PERFORMANCE
Performance of an expert Performance of
IOTA models
Performance of
old models
Performance of
non-experts
You share, we care !

19. PREDICTIVE

20. PERSONALIZED
LOGIC-Insulin
with
Tight Glycemic Control in intensive care lowers mortality
Control algorithm LOGIC-Insulin (‘automated customized patient pilot’)
400 clinical trials

22. PREVENTIVE
ACACATTAAATCTTATATGCTAAAACTAGGTCTCGT
TTTAGGGATGTTTATAACCATCTTTGAGATTATTGA
TGCATGGTTATTGGTTAGAAAAAATATACGCTTGTT
TTTCTTTCCTAGGTTGATTGACTCATACATGTGTTT
CATTGAGGAAGGAACTTAACAAAACTGCACTTTTT
TCAACGTCACAGCTACTTTAAAAGTGATCAAAGTA
TATCAAGAAAGCTTAATATAAAGACATTTGTTTCAA
GGTTTCGTAAGTGCACAATATCAAGAAGACAAAAA
TGACTAATTTTGTTTTCAGGAAGCATATATATTACA
CGAACACAAATCTATTTTTGTAATCAACACCGACCA
TGGTTCGATTACACACATTAAATCTTATATGCTAAA
ACTAGGTCTCGTTTTAGGGATGTTTATAACCATCTT
TGAGATTATTGATGCATGGTTATTGGTTAGAAAAAA
TATACGCTTGTTTTTCTTTCCTAGGTTGATTGA
ACACATTAAATCTTATATGCTAAAACTAGGTCTCGT
TTTAGGGATGTTTATAACCATCTTTGAGATTATTGA
TGCATGGTTATTGGTTAGAAAAAATATACGCTTGTT
TTTCTTTCCTAGGTTGATTGACTCATACATGTGTTT
CATTGAGGAAGGAACTTAACAAAACTGCACTTTTT
TCAACGTCACAGCTACTTTAAAAGTGATCAAAGTA
TATCAAGAAAGCTTAATATAAAGACATTTGTTTCAA
GGTTTCGTAAGTGCACAATATCAAGAAGACAAAAA
TGACTAATTTTGTTTTCAGGAAGCATATATATTACA
CGAACACAAATCTATTTTTGTAATCAACACCGACCA
TGGTTCGATTACACACATTAAATCTTATATGCTAAA
ACTAGGTCTCGTTTTAGGGATGTTTATAACCATCTT
TGAGATTATTGATGCATGGTTATTGGTTAGAAAAAA
TATACGCTTGTTTTTCTTTCCTAGGTTGATTGA
ACACATTAAATCTTATATGCTAAAACTAGGTCTCGT
TTTAGGGATGTTTATAACCATCTTTGAGATTATTGA
TGCATGGTTATTGGTTAGAAAAAATATACGCTTGTT
TTTCTTTCCTAGGTTGATTGACTCATACATGTGTTT
CATTGAGGAAGGAACTTAACAAAACTGCACTTTTT
TCAACGTCACAGCTACTTTAAAAGTGATCAAAGTA
TATCAAGAAAGCTTAATATAAAGACATTTGTTTCAA
GGTTTCGTAAGTGCACAATATCAAGAAGACAAAAA
TGACTAATTTTGTTTTCAGGAAGCATATATATTACA
CGAACACAAATCTATTTTTGTAATCAACACCGACCA
TGGTTCGATTACACACATTAAATCTTATATGCTAAA
ACTAGGTCTCGTTTTAGGGATGTTTATAACCATCTT
TGAGATTATTGATGCATGGTTATTGGTTAGAAAAAA
TATACGCTTGTTTTTCTTTCCTAGGTTGATTGA

23. GENOMIC DATA FUSION
High-throughput
genomics
Data analysis Candidate genes
Information sources
Candidate prioritization
Validation
Endeavour: Aerts et al., Nature Biotechnology, 2006

24. PROFESSIONAL
genomic data fusion:
trace disease-causing variants
20x more accurate
Bron:
De Tijd, woensdag 23 oktober 2013
Sifrim, Popovic et al,
Nature Methods, 2013
http://homes.esat.kuleuven.be/~bioiuser/eXtasy/

25. PREDICTIVE
Genetic Biomarkers
For Leukemia
© 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)

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

27. POLICY
Social security data mining
for 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. prescription
behaviour)
• Mine RIZIV ? !

28. iMinds
MEDICAL IT
www.iminds.be/medicalit
Trends
P3 x P4 medicine
Decision support
cases