prof. dr. Bart De Moor : ESAT-STADIUS KULeuven, Scientific Director of iMinds Department Future Health

1. Toekomstvisie op ICT in de
gezondheidszorg
Prof. Dr. Bart De Moor
Care for Innovation
Healthcare Conference
12 November 2013, Affligem
www.esat.kuleuven.be/stadius
www.kuleuven.be/iminds/fh

2. Contents
• Disruptive science and technology
• 3P/4P Medicine
– Preventive, Predictive, Personalized, Participatory
– Patients, Professionals, Policy Makers
• Exemplary cases
• Future Trends

3. www: max 19 clicks !

4. 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

5. Connectivity
We are always
CONNECTED
and FAST!
Throughput NOT sufficient for
moving around Big Data
5

6. Tsunami of data from progress in technology
GS-FLX Roche
Applied Science 454
Sequencers
Computer Tomography
Magnetic resonance
Microarrays
(DNA chips)
ACACATTAAATCTTATATGC
TAAAACTAGGTCTCGTTTTA
GGGATGTTTATAACCATCTT
TGAGATTATTGATGCATGGT
TATTGGTTAGAAAAAATATA
CGCTTGTTTTTCTTTCCTAG
GTTGATTGACTCATACATGT
GTTTCATTGAGGAAGGAAC
TTAACAAAACTGCACTTTTT
TCAACGTCACAGCTACTTTA
AAAGTGATCAAAGTATATCA
AGAAAGCTTAATATAAAGAC
ATTTGTTTCAAGGTTTCGTA
AGTGCACAATATCAAGAAG
ACAAAAATGACTAATTTTGT
TTTCAGGAAGCATATATATT
ACACGAACACAAATCTATTT
TTGTAATCAACACCGACCAT
GGTTCGATTACACACATTAA
ATCTTATATGCTAAAACTAG
GTCTCGTTTTAGGGATGTTT
ATAACCATCTTTGAGATTAT
TGATGCATGGTTATTGGTTA
GAAAAAATATACGCTTGTTT
TTCTTTCCTAGGTTGATTGA
Mass spectrometry

7. Genome data
• Human genome project
– Initial draft: June 2000
– Final draft: April 2003
– 13 year project
– $300 million value
with 2002 technology
• Personal genome
YearΩ
– June 1, 2007
– Genome of James Watson,
co-discoverer of DNA double
helix, is sequenced
• $1.000.000
• Two months
• €1000-genome
– Expected 2012-2020
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
1,00E-07
Cost per base pair
Genome cost
1990
1995
2000
2002
2005
2007
Cost per base pair
2010
2015
Genome cost
1990
10
3E+10
1995
1
3.000.000.000
2000
0.2
600.000.000
2002
0.09
270.000.000
2005
0.03
90.000.000
2007
0.000333333
1.000.000
2010
3.33333E-06
10000
2015
0.0000001
300
7

8. index of 20
million
Biomedical
PubMed
records
1 slice
mouse brain
MSI at 10
μm
resolution
81 GigaByte
raw NGS data
of 1 full
genome
sequencing all newborns
by 2020 (125k births /
year)
125 PetaByte / year
1 TeraByte
23 GigaByte
1 kB = 1000
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 small
animal
image
1 CDROM
750
MegaByt
e
1 GigaByte
PACS UZ Leuven
1,6 PetaByte
Genomics core
HiSeq 2000 full
speed exome
sequencing
1 TeraByte /
week
8

9. Contents
• Disruptive science and technology
• 3P/4P Medicine
– Preventive, Predictive, Personalized, Participatory
– Patients, Professionals, Policy Makers
• Exemplary cases
• Future Trends

10. ICT tools for decision support
ICT to support 3P decision makers in health care
• Professionals (clinicians, biomedical researchers …)
• exploit data for more effective medicine
• Patient
• patient-centered care for empowered patients
• Policy Maker (mutualities, health insurers, social security,
governments, hospital management …)
• Smart, data-driven, evidence-based health care system policies
10

11. ICT contributes to P4 medicine
• Personalized: "customized" diagnosis and treatment, right
drug for the right patient at the right time
• Preventive: prevention and early diagnosis
• Predictive: determine risk profiles, predict progression and
outcome
• Participatory: correct and complete information for the
patient to participate in the decision process, selfmanagement
11

12. Rationales for ICT & Health
-Improve quality performance of health decision/diagnosis systems
-Support individual medical doctor
-Avoid/decrease number of medicial errors
-Web portal for Evidence Based Medicine
-Information sharing among doctors
-avoid/monitor patient (s)hopping behavior
-Global Medical File per patient
-Interoperability
-Deal with ‘empowerment of the patient’: Patient-centric health care
-Medical care in 4P: personalized, preventive, predictive, participatory
-Increasing trend for ‘customized’’personalized’ medicine
-Improve transparancy and consistency
-Deal/cope with ‘professional’ (chronical) patients (heart, diabetes, cancer,…)
-Improve patient mobility
-Cost effectiveness of the health care system
-Ageing population:
-EU 2050: 65+  +70%; 80+  +180%
-Monitor overconsumption
-Improve transparancy
-Detect abnormalities in diagnosis/therapy/…
-Cope with tsunami of available information and data (clinical, population, ….)

13. Contents
• Disruptive science and technology
• 3P/4P Medicine
– Preventive, Predictive, Personalized, Participatory
– Patients, Professionals, Policy Makers
• Exemplary cases
• Future Trends

14. NXT_SLEEP: Next generation sleep
monitoring platform
14

15. FallRisk: Intelligent services in view of prevention,
risk evaluation and detection of fall incidents
15

16. Blood Glucose Control @ ICU
 10 mio adult ICU patients / year (EU + US) (1-2 bio $ market)
 Intensive Care Unit UZ Leuven studies: Tight Glycemic Control (TGC) in
intensive care unit lowers mortality
 LOGIC-Insulin: semi-automatic control system that advises nurse on
insulin dosage and blood sampling interval aiming at TGC and avoiding
hypoglycemia
 clinical trial: compared with expert nurses, LOGIC-Insulin showed
improved efficacy of TGC without increasing rate of hypoglycemia
 multicentre clinical trial Q3 2013; spin-off in preparation
16

17. IOTA app: ovarian tumour malignancy:
population based / standardized
IOTA Models:
Standardize ultrasonographic endometrial tumor
analysis
models giving an indication of the probability of
malignancy of an ovarian tumour based on 6 to 12
observed parameters
IOTA app available in iTunes app store and on
http://homes.esat.kuleuven.be/~sistawww/biomed/iota/
17

18. IOTA app: Clinical Decision Support
 goal = making it easier to diagnose ovarian cancer
 help clinicians take decisions on diagnosis, prognosis and therapy response
 make use of patient data ánd population information (patient biobank &
database, literature …)
 build mathematical model on data and use this model to predict patient
outcome: formalized way of analyzing biomedical data instead of
interpretation of clinical parameters based on a clinician’s expert knowledge
 model method can be varied: logistic regression, artifical neural networks,
Support Vector Machines (SVM), Bayesian networks …
Patient history
Tumor
characteristics
Clinician
Ultrasound
characteristics
Tumor markers
Diagnosis
Prognosis
Model
Response to
therapy

19. IOTA – performance comparison
Performance of an expert
Performance of
IOTA models
Performance of
non-experts
Performance of
old models

20. Microarrays – DNA-chips
Test Ref.
High Low
Low
High
High High
Low
Low

21. Genetic classification of leukemia
12600 genes / 72 patients / 3 types of leukemia (AML/ ALL/MLL)
Data matrix
Find the pattern
Hidden pattern
Pattern validation

22. 18 / 21 patients with 87 gene fingerprint for AML
19 / 25 patients with 80 gene fingerprint for ALL
14 / 17 patients with 62 gene fingerprint for MLL

23. ALL/AML/MLL biomarkers
PCA
© 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)
3 patients for each class used as test set

24. Aerts et al, Nature Biotechnology, 2006
24

25. 25

26. 26

27. Variant prioritization by genomic data fusion:
including disease phenotypes
Figure – Classification scenarios. Receiver-operator (A and C)
and Precision-Recall (B and D) curves for five classifiers tested
against test sets comparing disease-causing variants to either
rare non-disease-causing variants (A and B) or common
polymorphisms (C and D).
based on Random Forests
20x more accurate than state of the art
Sifrim, Popovic et al, Nature Methods, 2013d

28. Mass Spectral Imaging
•
Mass spectral imaging allows
•
•
•
•
Screening and mapping of hundreds of biomolecules in a complex organic tissue
section
Does not require any prior information nor labeling
Visualization of the spatial distribution of these molecules
Computation complexity
•
•
The structure of the generated data is complex
Large size and high dimensionality
in collaboration with SyBioMa, KU Leuven Facility
for Systems Biology based Mass Spectrometry,
MIRC KU Leuven & Caprioli Lab (Vanderbilt
University, VS)

29. Contents
• Disruptive science and technology
• 3P/4P Medicine
– Preventive, Predictive, Personalized, Participatory
– Patients, Professionals, Policy Makers
• Exemplary cases
• Future Trends

30. Moore’s law: Computing power doubles
every 18 months (56 % growth rate)
‘Understand’ ?
6 10 9
Operations/second
5 10 9
LUI
4 10 9
3D games
3 10
9
2 10 9
1 10 9
Bookkeeping
0
1975
1980
Audio
1985
1990
Video
1995
Year
2000
2005
2010

31. Body area networks
EEG
POTS
Vision
www Network
positioning
glucose
Cellular
Transducer
Nodes
Hearing
ECG
Blood
pressure
DNA
protein
Implants
WLAN
Personal
Assistant
31

32. 1953 Crick and Watson: DNA Double Helix
2000 Venter, Collins: Humane genome

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

34. building on expertise in
big data & machine
learning
BENCH is used in ca. 50 accredited
genetic labs worldwide
Heverlee + US office
routine diagnostics
tools for non-IT users
first diagnostics grade solution
for Next Generation
Sequencing data based
diagnostics in the world
originating from research in
rare genetic disorders in close
collaboration with Centre
Human Genetics UZ Leuven 34

35. Systems biology: ‘reverse engineering’
‘high throughput ‘data
genome
transcriptome
proteome
metabolome
interactome
35

36. Zebrafish Neural Circuits
 understand dynamical behaviour of neural circuits in Zebrafish
through system identification
u
y = f (u )
y
Input
RAW STIMULUS
Output
BRAIN
Model
Identification
Algorithm
REACTION
-
in collaboration
with NERF

37. Signal Processing and Classification for Magnetic
Resonance Spectroscopy Applied to Brain Tumor
Diagnosis
Single-voxel MRS
MRS quantification
NAA
Myo
Cr
PCho
Glu
Lac
Lip1
Lip2
Ala
Glc
Metabolite
concentrations
Tau
Biomarker
of disease
Metabolite maps
Multi-voxel MRS
MRS quantification
using spatial information

38. Seizure detection incorporating structural
information from the multichannel EEG
Representation of EEG data in
higher order arrays:
features
channels
frequency
channels
features
channels
channels
time
Classifier matrix
Goal: Exploit the inherent structure
of the EEG signals using novel matrix
and tensor-based machine learning
solutions:
– Nuclear norm regularization
– Tensorial kernels

39. Collaboration IMEC/NERF:
neuroprobes
MULTI-FUNCTIONAL INTEGRATED
NEUROPROBES
… ADVANCED & AUTOMATED
MULTICHANNEL SIGNAL
PROCESSING..
…. INTEGRATED END-USER FEEDBACK

40. Synthetic biology: design new functional life
Bacterium detecting cancer cells
Output
Input
Reset
Memory
Filter
Cell Death
InverTimer
http://2013.igem.org/Team:KU_Leuven

41. Obama
http://www.whitehouse.gov/blog/09/04/27/The-Necessity-of-Science/
…
The Recovery Act will support the long overdue step of computerizing America's medical records, to reduce the
duplication, waste and errors that cost billions of dollars and thousands of lives. But it's important to note, these
records also hold the potential of offering patients the chance to be more active participants in the prevention and
treatment of their diseases. We must maintain patient control over these records and respect their privacy. At the
same time, we have the opportunity to offer billions and billions of anonymous data points to medical researchers
who may find in this information evidence that can help us better understand disease.
….
Because of recent progress –- not just in biology, genetics and medicine, but also in physics, chemistry, computer
science, and engineering –- we have the potential to make enormous progress against diseases in the coming
decades.
….
41

42. Decision Support Analytics
for Professionals, Patients & Policy makers
bart.demoor@esat.kuleuven.be
www.esat.kuleuven.be/stadius
www.kuleuven.be/iminds/fh