1 / 39

PSIP Workshop Belgirate, Italy, 24-25 September 2009

DebugIT : Building a European distributed clinical data mining network to foster the fight against microbial diseases. PSIP Workshop Belgirate, Italy, 24-25 September 2009. Christian Lovis, Teodoro Douglas, Emilie Pasche, Patrick Ruch, Dirk Colaert, Karl Stroetmann.

sibyl
Télécharger la présentation

PSIP Workshop Belgirate, Italy, 24-25 September 2009

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. DebugIT: Building a European distributed clinical data mining network to foster the fight against microbial diseases PSIP Workshop Belgirate, Italy, 24-25 September 2009 • Christian Lovis, Teodoro Douglas, Emilie Pasche,Patrick Ruch, Dirk Colaert, Karl Stroetmann • Presented byKarl Stroetmann

  2. C o n t e n t s • The project • Conceptual framework & technology • Challenges • Clinical & socio-economic impact assessment • 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 ICTfor 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 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 Apply Learn 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 Adverse event Adverse event Health care setting Prescription Antibiogram Diseases Culture Patient data Pathogen Lead by INSERM

  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 HUG INSERM LiU AVERBIS Ready Almost ready Good progress

  23. Activities and progress Knowledge authoring tool : Generation assistant The user writessomeparameters Differentmethods of generation List of recommandations

  24. Activities and progress Knowledge authoring tool: Validation assistant The user writes a rule Differentmethods of validation Trend-based validation Text-based validation

  25. SQL endpoint: multiple site visualization SQL Demonstration of CDR: query distributed between LiU and HUG Yearly resistance of Ecoli to TMP/SMX HUG LiU

  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 Indicators Data collection methods 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 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.

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

More Related