Transcript PSIP Workshop

•DebugIT: Building a European distributed
clinical data mining network to foster the
fight against microbial diseases
•Christian Lovis, Teodoro Douglas, Emilie Pasche,
Patrick Ruch, Dirk Colaert, Karl Stroetmann
•Presented by Karl Stroetmann
PSIP Workshop
Belgirate, Italy, 24-25 September 2009
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PSIP Workshop, Beligrate, Italy, 24 - 25 September 2009
Contents
1. The project
2. Conceptual framework & technology
3. Challenges
4. Clinical & socio-economic impact
assessment
5. Outlook
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PSIP Workshop, Beligrate, Italy, 24 - 25 September 2009
The DebugIT project
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Funding and time schedule
EU funded IP (integrated project)
FP7 (Framework Programme 7) - a research initiative of
the European Union
The DebugIT project proposal was ranked first
Start date:
End date:
Jan 1st, 2008
December 31st, 2011
11 Partners
11 Work packages
Total EU funding of the project €7m
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The Partners
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2
3
4
5
6
7
Agfa
HUG
UNIGE
LIU
EMP
UCL
INSERM
8 UKLFR
9 TEILAM
10 IZIP
11 GAMA
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Agfa HealthCare N.V., Belgium
Les Hôpitaux universitaires de Genève, Switzerland
Université De Genève, CH
LINKÖPINGS UNIVERSITET, Sweden
empirica, Bonn, Germany
University College London, UK
Institut National de la Santé et de la Recherche
Médicale, Paris, France
Universitätsklinikum Freiburg, Germany
TECHNOLOGIKO EKPEDEFTIKO IDRIMA LAMIAS,
Greece
IZIP A.S., Prague, Czech Republic
Gama/Sofia Ltd., Sofia, Bulgaria
PSIP Workshop, Beligrate, Italy, 24 - 25 September 2009
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Overview
• DebugIT: Detecting and Eliminating Bacteria UsinG
Information Technology
• Dedicated to infectious diseases
• Aims:
– detecting patient safety related patterns and trends
– acquiring new knowledge
– using this for better quality healthcare
• Consortium of eleven partners across the EU
• Strong clinical lead assured by
– Clinical Advisory Board (President: Prof. Dr. Didier
Pittet, HUG, Geneva; World Alliance for Patient Safety,
World Health Organisation)
– Scientific Advisory Board
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Objectives
• Built an advanced tool aiming at infectious
pathogens across health systems and
levels
• Integrate it into clinical information
systems of participating European
hospitals
• Develop generic conceptual base that can
be easily expanded to other similar
medical fields
• Make the tool publicly available
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Why infectious diseases ?
• Advanced ICT for Risk Assessment and Patient
Safety project
-> main focus on advanced ICT
• Risk assessment and patient safety on a 4 years
project
-> a coherent choice: infectious diseases
1.
2.
3.
usually short life cycles
measurable results
data available on the whole range of semantic and
technical complexity
lab results, order entry, structured text, free text, images
4.
5.
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hot topic for public health and clinical research
can provide decision support for research, clinicians and
governance
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Clinical context
Antibiotic resistance is a consequence
of evolution via natural selection
Antibiotic action is urgently needed to respond
to environmental pressure:
• Patterns of antibiotic usage greatly affect the number
of resistant organisms which develop
• Overuse of broad-spectrum antibiotics
• Incorrect diagnosis
• Unnecessary prescriptions
• Improper use of antibiotics
• Use of antibiotics as livestock food additives for
growth promotion
• Counterfeit drugs
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Clinical context
antibiotic resistance in Salmonella typhimurium DT104, England and
Wales, 1984-1995
WHO Weekly Epidemiological Record,
Vol 71, No 18, 1996
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Main focus for Y2: “Closing the
Loop As Soon As Possible”
 Interoperability platform (WP1)
 Data Normalization (WP2)
 Data Analysis (WP3)
 Knowledge extraction (WP3/WP4)
 Knowledge authoring (WP4)
 Inference tools (WP5)
 Clinical decision-support (WP6)
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Conceptual framework
& technology
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Iterative Cycle
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•
•
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collect routinely stored data from clinical systems
learn by applying advanced data mining techniques
store the extracted knowledge in repositories
apply knowledge for decision support and monitoring
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Iterative Cycle
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Collect – clinical data repository
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Routinely stored clinical data is collected and
aggregated across
–
–
–
–
–
•
hospitals
countries
languages
information models
legislations
Collect
via
–
–
–
–
commonly agreed data models (minimal data sets)
standards
mapping algorithms
unified and enhanced ontologies
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Learn – multimodal data mining
•
•
detect relevant patterns
advanced data mining techniques on
multimodal & multi-source data
– structured data mining
– text mining
– image mining
•
Collect
Learn
create new knowledge using advanced
multimodal knowledge-driven data mining
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Store – medical knowledge
repository
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knowledge is
– stored in a distributed repository
– validated by clinicians
Collect
– visualised and aggregated together with
pre-existing medical and
biological knowledge (guidelines, regulations)
•
Learn
a consolidated organization
in the knowledge repository
Store
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Apply – decision support tools
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•
software tools integrated in clinical and public health
information systems
decision support tools
– apply generated knowledge
– help clinicians to provide clinical care
– example: choice, dose and
administration of antibiotics
Apply
– predict future outcomes
•
Collect
Learn
monitoring tools for
– research
– epidemiology
– health policy
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PSIP Workshop, Beligrate, Italy, 24 - 25 September 2009
Store
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Translational and evidence based
medicine
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•
•
•
DebugIT is a nice example of translational medicine
and evidence based medicine
clinical care uses knowledge and
Collect
evidences from research
(bench to bed)
research uses
Apply
Learn
real life clinical data
(bed to bench)
Store
access to huge amounts
of real-world data is a welcome addition
to expensive traditional clinical studies
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Activities and progress: HL7-RIM
based common schema
•Adverse event•Adverse event
•Prescription
•Health care setting
•Diseases •Antibiogram
•Patient data
Lead by INSERM
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•Culture
•Pathogen
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DebugIT CDR architecture
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Data integration via database federation
SQL
endpoint
AVERBIS
H
Ready
Almost ready
Good progress
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U
G
INSERM
L
i
U
1. First implementation using
low performance machine:
• Many problems with
performance
• Constant use of disk
temporary tables,
indexes problems
(losing key because
disk was full)
2. Change to a better server
with 8 GB of memory, 4
processors, SCSI drivers:
• Query speed has
improved significantly
• Complex queries
between 2 centres
executed in ~1 min
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Activities and progress
Knowledge authoring tool :
Generation assistant
The user writes some parameters
Different methods of
generation
List of recommandations
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Activities and progress
Knowledge authoring tool:
Validation assistant
The user writes a rule
Different methods
of validation
Text-based validation
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Trend-based
validation
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Demonstration
of CDR: query
distributed
between LiU
and HUG
Yearly resistance of Ecoli to TMP/SMX
SQL
HUG
L i U
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Challenges
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Interoperability
• Language independent formal vocabulary as
input for data analysis & data mining
• Formal semantics and textual descriptions to
precisely describe abstracted meanings
• Extraction of heterogeneous structured and
unstructured EPR content
• Semantic standard for project-wide
information Clinical Data Repository
Formalism
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Data mining
• Data aggregation from heterogeneous sources
• Management of data quality and reliability
• Integration and mining of multimodal data,
including images
• Knowledge-driven data mining
• Advanced data mining, (bio)statistics, signal
theory, lexical analysis and ontological
analysis
• Multi-axial mining, temporal, multimodal, case
and cohort base
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Knowledge and inference
• Federated knowledge repository
– heterogeneous sources, variable level of
certainty
– representation of knowledge and rules
• Reasoning
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–
–
–
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statistical + logical
performance
formalism and decidability
reliability for case based decision support
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Example of data mining challenge
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Impact assessment
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Impact assessment framework
Project evaluation
Outcome assessment
impact on scientific community
impact on EC initiatives
...
cost benefit analysis
clinical impact (DSS)
technology
What to
measure
& why
Indicators
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What to
measure
& why
How to
measure
Measurements
Data collection
methods
PSIP Workshop, Beligrate, Italy, 24 - 25 September 2009
How to
measure
Indicators Measurements
Data collection
methos
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Project evaluation
• Impact on scientific community
–
–
–
–
–
Value of individual project outputs
Transferability to other research areas
Type of scientific progress achieved
Impact on research capacity
Impact on efficiency of future research
• Impact on scientific & technological
objectives of EC initiatives with regards to:
–
–
–
–
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Macroeconomic development
Private Sector (Industry & SMEs)
Research initiatives
Health Sector / eHealth
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Outcome assessment
• Clinical and socio-economic impact assessment
based on benefit & cost analysis
• Identification of positive (benefits) & negative
(costs) impacts to all relevant stakeholders
• Quantification in terms of monetary units in order
to derive total net benefit for society
• Development of individual scenarios based on life
cycle approach, time horizons, and diffusion speed
• Capturing uncertainty/risk and prospective nature
of analysis
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Risk & uncertainty
•Range of
probable
outcomes:
Probability Distribution of Net Benefits
€30,000,000.00
€25,000,000.00
Net Benefits
€20,000,000.00
€15,000,000.00
•90% •99%
€10,000,000.00
€5,000,000.00
€2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
•Mean
-€5,000,000.00
Years
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Outlook
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Summary
• Focus on large existing, heterogeneous clinical data
repositories
• Building an interoperability platform that is usable for
the whole infectious domain
• Creation of a federated clinical data repository that
enables knowledge-driven data mining
• Leverage of patient data with existing knowledge and
merger into a clinical knowledge repository
• Exploitation of newly generated knowledge with a
clinical decision support system to loop back to clinical
practice
• Serious advance in building a large IT infrastructure
creating knowledge in the fight against infectious
diseases
• Reusable for other diseases and contexts
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Acknowledgement and disclaimer
 DebugIT is a project co-funded by the European
Commission’s Seventh FRAMEWORK PROGRAMME.
 The research reported upon in this presentation has
either directly or indirectly been supported by the
European Commission, Directorate General
Information Society and Media, Brussels.
 The results, analyses and conclusions derived there
from reflect solely the views of its authors and of the
presenter.
 The European Community is not liable for any use that
may be made of the information contained therein.
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Thank you for your attention
More info ?
http://www.debugit.eu
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