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PSIP Workshop

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DebugIT: Building a European distributed clinical data mining network to foster the fight against microbial diseases Christian Lovis, Teodoro Douglas, Emilie Pasche, – PowerPoint PPT presentation

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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

2
C o n t e n t s
  1. The project
  2. Conceptual framework technology
  3. Challenges
  4. Clinical socio-economic impact assessment
  5. Outlook

3
The DebugIT project
4
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 Jan 1st, 2008
  • End date December 31st, 2011
  • 11 Partners
  • 11 Work packages
  • Total EU funding of the project 7m

5
The 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

6
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

7
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

8
Why 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

9
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

10
Clinical context
antibiotic resistance in Salmonella typhimurium
DT104, England and Wales, 1984-1995
WHO Weekly Epidemiological Record, Vol 71, No
18, 1996
11
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)

12
Conceptual framework technology
13
Iterative 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

14
Iterative Cycle
15
Collect 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
16
Learn 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
17
Store 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
18
Apply 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
19
Translational 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
20
Activities and progress HL7-RIM based common
schema
21
DebugIT CDR architecture
22
Data 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
23
Activities and progress
  • Knowledge authoring tool
  • Generation assistant

The user writes some parameters
Different methods of generation
List of recommandations
24
Activities and progress
  • Knowledge authoring tool
  • Validation assistant

The user writes a rule
Different methods of validation
Trend-based validation
Text-based validation
25
SQL endpoint multiple site visualization
Demonstration of CDR query distributed between
LiU and HUG
Yearly resistance of Ecoli to TMP/SMX
26
Challenges
27
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

28
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

29
Knowledge 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

30
Example of data mining challenge
31
Impact assessment
32
Impact 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
33
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
  • Macroeconomic development
  • Private Sector (Industry SMEs)
  • Research initiatives
  • Health Sector / eHealth

34
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

35
Risk uncertainty
  • Range of probable outcomes
  • 99
  • 90
  • Mean

36
Outlook
37
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

38
Acknowledgement 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.

39
Thank you for your attention
More info ? http//www.debugit.eu
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