Title: PSIP Workshop
1- 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
2C o n t e n t s
- The project
- Conceptual framework technology
- Challenges
- Clinical socio-economic impact assessment
- Outlook
3The DebugIT project
4Funding 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 Jan 1st, 2008
- End date December 31st, 2011
- 11 Partners
- 11 Work packages
- Total EU funding of the project 7m
5The Partners
- 1 Agfa Agfa HealthCare N.V., Belgium
- 2 HUG Les Hôpitaux universitaires de Genève,
Switzerland - 3 UNIGE Université De Genève, CH
- 4 LIU LINKÖPINGS UNIVERSITET, Sweden
- 5 EMP empirica, Bonn, Germany
- 6 UCL University College London, UK
- 7 INSERM Institut National de la Santé et de la
Recherche Médicale, Paris, France - 8 UKLFR Universitätsklinikum Freiburg, Germany
- 9 TEILAM TECHNOLOGIKO EKPEDEFTIKO IDRIMA LAMIAS,
Greece - 10 IZIP IZIP A.S., Prague, Czech Republic
- 11 GAMA Gama/Sofia Ltd., Sofia, Bulgaria
6Overview
- 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
7Objectives
- 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
8Why infectious diseases ?
- Advanced ICT for Risk Assessment and Patient
Safety project - -gt main focus on advanced ICT
- Risk assessment and patient safety on a 4 years
project - -gt a coherent choice infectious diseases
- 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 - hot topic for public health and clinical research
- can provide decision support for research,
clinicians and governance
9Clinical 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
10Clinical context
antibiotic resistance in Salmonella typhimurium
DT104, England and Wales, 1984-1995
WHO Weekly Epidemiological Record, Vol 71, No
18, 1996
11Main 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)
12Conceptual framework technology
13Iterative Cycle
- 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
14Iterative Cycle
15Collect clinical data repository
- Routinely stored clinical data is collected and
aggregated across - hospitals
- countries
- languages
- information models
- legislations
- via
- commonly agreed data models (minimal data sets)
- standards
- mapping algorithms
- unified and enhanced ontologies
Collect
16Learn multimodal data mining
- detect relevant patterns
- advanced data mining techniques on multimodal
multi-source data - structured data mining
- text mining
- image mining
- create new knowledge using advanced multimodal
knowledge-driven data mining
Collect
Learn
17Store medical knowledge repository
- knowledge is
- stored in a distributed repository
- validated by clinicians
- visualised and aggregated together with
pre-existing medical and biological knowledge
(guidelines, regulations) - a consolidated organization in the knowledge
repository
Collect
Learn
Store
18Apply decision support tools
- 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 - predict future outcomes
- monitoring tools for
- research
- epidemiology
- health policy
Collect
Learn
Apply
Store
19Translational and evidence based medicine
- DebugIT is a nice example of translational
medicine and evidence based medicine - clinical care uses knowledge and evidences from
research(bench to bed) - research uses real life clinical data (bed to
bench) - access to huge amounts of real-world data is a
welcome addition to expensive traditional
clinical studies
Collect
Apply
Learn
Store
20Activities and progress HL7-RIM based common
schema
21DebugIT CDR architecture
22Data integration via database federation
SQL endpoint
- First implementation using low performance
machine - Many problems with performance
- Constant use of disk temporary tables, indexes
problems (losing key because disk was full) - 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
Ready
Almost ready
Good progress
23Activities and progress
- Knowledge authoring tool
- Generation assistant
The user writes some parameters
Different methods of generation
List of recommandations
24Activities and progress
- Knowledge authoring tool
- Validation assistant
The user writes a rule
Different methods of validation
Trend-based validation
Text-based validation
25SQL endpoint multiple site visualization
Demonstration of CDR query distributed between
LiU and HUG
Yearly resistance of Ecoli to TMP/SMX
26Challenges
27Interoperability
- 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
28Data 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
29Knowledge and inference
- Federated knowledge repository
- heterogeneous sources, variable level of
certainty - representation of knowledge and rules
- Reasoning
- statistical logical
- performance
- formalism and decidability
- reliability for case based decision support
30Example of data mining challenge
31Impact assessment
32Impact assessment framework
- Project evaluation
- impact on scientific community
- impact on EC initiatives
- ...
- Outcome assessment
- cost benefit analysis
- clinical impact (DSS)
- technology
What to measure why
How to measure
What to measure why
How to measure
Measurements
Data collection methods
Indicators
Measurements
Indicators
Data collection methos
33Project 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 - Macroeconomic development
- Private Sector (Industry SMEs)
- Research initiatives
- Health Sector / eHealth
34Outcome 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
35Risk uncertainty
- Range of probable outcomes
36Outlook
37Summary
- 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
38Acknowledgement and disclaimer
- DebugIT is a project co-funded by the European
Commissions 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.
39Thank you for your attention
More info ? http//www.debugit.eu