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Future Health
Decision Support for
Professionals, Patients & Policy
Bart De Moor
ESAT-SCD-SISTA, ESAT-PSI, CUO, ITEC
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• IBBT = Interdisciplinary Institute for Broadband Technology
• 1 out of 4 strategic research centers (SOC) in Flanders
• Virtual: expertise of university research groups
• Link between research and industry
� Innovative ICT services and applications in close collaboration with government and industry
IBBT
technology driven
society driven
research driven
demand driven
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Future Internet
Future Media
Security
Future Health
Digital Society
IBBT Structure: 5 research departments
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Associated research groups
• bioinformatics & machine learning
• biomedical data processing
• digital signal processing for audio & telecom
• Fac. EngineeringDept. ESAT - K.U.Leuven
• user experienceresearch
• Fac. Social SciencesK.U.Leuven
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Structure department
Scientific
Committee
Research
Managers
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Societal trends
Patient empowerment � P4 Medicine• Personalized
• Preventive
• Predictive
• Participatory
���� challenge: patient-centric &personalized health care
AgeingFlanders 2012
���� challenge: reconcile quality with costs
60plus age related &
chronic diseasesrise
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Societal trends
Patient empowerment � P4 Medicine• Personalized: right treatment for right patient at right time
• Preventive: avoid high costs in curation
• Predictive: determine risk profiles and predict progression and outcome
• Participatory: patient is central and active shareholder in health care
���� challenge: patient-centric &personalized health care
AgeingFlanders 2012
���� challenge: reconcile quality with costs
60plus age related &
chronic diseasesrise
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Technological trendsIT + internet
performance &
pervasiveness
Moore’s Law�
computingpower
broadband capacity & pervasiveness� data access / sharing
Monitoring &
smart systems
• Variety of imaging modalities
• Increasing resolution
• Multichannel, Mobile & Real-Time
Biomedical
technologies
Carlson’s law �$1000 personal genome
Biobank
supercomputing
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Towards evidence-based and personalized medicine
access to clinical knowledge of populations & individuals (e.g. diagnostic tools)
low
low
high
high
trial and error
evidencebased
individualclinical knowledgeand experience
clinicianconsensus
evidence-based(based on population)
personalized(based on people like me)
clinical practice
access to patient
information
health care innovation
You share, we care!
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Challenge: data tsunami
interpretability complexity
# data, e.g. genetic
Price per data point, e.g sequenced base pair
analysis bottleneck
IT to the rescue!
IT, mathematical engineering and software design
� fully exploit the opportunities created by advancing technologies
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• IT & software design
• data processing & mining
• data integration & visualization
• user experience & e-learning
to extract appropriate information
to transfer this information to the user:
professional, patient and policy maker
decision support to enable better health care
data: clinical, biomedical, imaging,
omics, health insurance data, medical knowledge …
IBBT Future Health Department:Health Decision Support
Professional
Patient
Policy
exploit data for more
effective medicine
patient-centred care for
empowered patients
smart & data-driven
health care system policies
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Research Focus
Professional / Clinical Decision Support Patient
DS
Policy
DS• interpretation of wide range of data & heterogeneous data fusion
• demand-driven, user-centred, with future vision
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Research Focus
Clinical
DS
Policy DS
• data mining to identify best practices, …
• hospital logistics
Patient Decision Support
‘patient empowerment’:e.g. disease management for patients with chronic diseases using new media
‘online’telemonitoring
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Research Focus
Clinical
DS
Policy
DS
Patient Decision Support
‘patient empowerment’:e.g. disease management for patients with chronic diseases using new media
‘online’telemonitoring
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Research Focus
Clinical
DS
Policy DS
• data mining
• trend detection
• clustering
• outlier detection
• to identify policy best practices, for hospital management, …
• nationaal kanker Registere, RIZIV-INAMI, Mutualities…
Patient
DS
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Future Health: Positioning
Health Care Information Systems
Invoicing
PACS/CWS
Health Insurance(e.g. RIZIV)
Health Decision Support
Clinical Decision Support
Patient Decision Support
Policy Decision Support
Fundamental Biomedical Research
Pathogenesis
Biomarkers
Target/DrugDiscovery
5-10 years ahead of deployment
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Research focus: track record
data handling & mining for clinical genetics
in silico
drugdiscovery
vision software & hardware
social interest software
medical image computing
(e-)learning solutions
image quantification
Theraplay: ergotherapeutic game
home monitoring for epilepsy detection
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Research focus: track record
• 17 PI, 25 postdocs, 88 PhD students
• ~ 270 publications /year
• ~ € 5 million external financing /year
• ~ 15 PhDs /year
• ~ 40 patents
• 5 spin-offs launched since 2005
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researchuniversities & research centres
strategic research centers: imec, VIB, VITO…
technology providers& core facilities
industrysupercomputing
medical imaging centerusability lab
sequencing (Genomics Core)…
health care stakeholderspatientshospitalsdoctors
health insurancehealth care policy
…
IBBTFuture Health
Interaction with stakeholders is essential for innovation in health care
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Cases
• IOTA: International Ovarian Tumour Analysis Group
• Endeavour: disease gene prioritization
• Epilepsy detection
• Tumor classification via MRS
• Semi-Automatic Blood Glucose Control in the ICU
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Cases
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Case: IOTA - International Ovarian Tumour Analysis Group
ESAT/SCD
� Making it easier to diagnose ovarian cancerClinicians have to make many decisions concerning the therapy of their patients e.g.:DiagnosisPrognosisTherapy response
• Based on expertise
• But often the clinician has– Patient Data
• Patient history
• Tumor characteristics
• Ultrasound characteristics
• Tumor markers
Patient history
Tumorcharacteristics
Ultrasoundcharacteristics
Tumor markers
Clinician Diagnosis
Prognosis
Response totherapy
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• Solution:
– Clinical decision support modeling
– Building a mathematical model on the data
– Use this model to predict patient outcome
• Diagnosis
• Prognosis
• Therapy response
Patient history
Tumorcharacteristics
Ultrasoundcharacteristics
Tumor markers
Clinician
Model
Diagnosis
Prognosis
Response totherapy
Case: IOTA - International Ovarian Tumour Analysis Group
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Performance of an expert Performance of IOTA models
Performance of old models
Performance of non-experts
IOTA models improve patient diagnosis in centers where no experts are available
This is the case for the majority of hospitals internationally
Case: IOTA - International Ovarian Tumour Analysis Group
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High-throughputgenomics
Data analysis Candidategenes
?Information sources
Candidate prioritization
Validation
ESAT/SCD
Case: Endeavour - disease gene prioritization
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Extract time and frequency domain features
Motion tracks
Extract features based on the motion tracks
Video-EEG monitoring Discriminate between
normal and epileptic events
Case: Epilepsy detection
ESAT/SCD
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Seizure localization
Parafac component corresponding to seizure
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Nuclear magnetic resonance imaging (NMR):
• “water images”• concentraion of
protons →anatomical details
Magnetic resonance spectroscopic imaging:
• Quantitative metabolite maps
Key challenges:• Accurate and fast quantitaion• Artifact removal• Automated classification 28
Case: Tumor classification via MRS
ESAT/SCD
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Case: Semi-Automatic Blood Glucose Control in the ICU
� Intensive Care Unit: critically ill patients - High insulin resistance leads to hyperglycemia in the ICU � need for Tight Glycaemic Control (TGC) = 80-110 mg/dl
� LOGIC-Insulin: algorithm and graphical user interface for normalizing blood glucose in critically ill patients in the ICU
LOGIC–
Insulin
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Clinical genomics
� next-generation sequencing: $1000 human genome will revolutionize clinical diagnostics
� genetic disorders
� risk analysis
� interaction with drugs (pharmacogenomics)
� but: huge amounts of data
� clinician � extract relevant informationin a timely manner!
� construct clinical data pipelines
� data mining & integration with other data sources
� visualization
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Heterogeneous data integration for
predictive modeling & biomarker discovery
� Example: IOTA = International Ovarian Tumour Analysis Group � 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 …)
Patient history
Tumorcharacteristics
Ultrasoundcharacteristics
Tumor markers
Clinician
Model
Diagnosis
Prognosis
Response totherapy
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Biomarker discovery through
Mass Spectral Imaging
� combines proteomic and metabolomic information with spatial distribution
� localize biomarkers in tissue
� discover new targets for drug delivery
� unravel mechanisms underlying disease
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� Intensive Care Unit: critically ill patients
� high insulin resistance leads to hyperglycemia in the ICU � need for Tight Glycaemic Control (TGC) = 80-110 mg/dl
� LOGIC-Insulin
� algorithm and graphical user for normalizing blood glucose in critically ill patients in the ICU
Semi-Automatic Blood Glucose Control
in the ICU
LOGIC–
Insulin
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Text & Data Mining
� Exploit information in text files– unstructured information
� disease related gene analysis in biomedical literature
� medical report annotation – e.g. use medical history to predict adverse drug effects
� Data mining of health care system data(RIZIV, mutualities …)
� trend detection
� clustering
� outlier detection
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Disease gene discovery by genomic data
fusion: EndeavourHigh-throughputgenomics
Data analysisCandidate genes
?
Information sources
Candidate prioritization
Validation
Aerts et al., Nature Biotechnology, 2006
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Network biology for drug profiling
�identify mode of action of drugs, adverse effects etc. through a network analysis of drug responses
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Epilepsy detection
Extract time and frequency domain features
Motion tracks
Extract features based on the motion tracks
Video-EEG monitoring Discriminate between
normal and epileptic events
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Seizure localization
Parafac component corresponding to seizure
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Tumor classification via MRS
Nuclear magnetic resonance imaging (NMR):
• “water images”• concentraion of
protons →anatomical details
Magnetic resonance spectroscopic imaging:
• Quantitative metabolite maps
Key challenges:• Accurate and fast quantitaion• Artifact removal• Automated classification
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Conclusion
Trends
• Dialogue
• Demand-driven
• User-centred
• Future vision
Clinical
Patient
Policy
exploit data for more
effective medicine
patient-centred care for
empowered patients
smart & data-driven
health care system policies
Decision Support for
Professionals, Patients & Policy
improve health care quality andcost effectiveness
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IBBT-K.U.Leuven
Future Health Department
www.kuleuven.be/ibbt
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