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Predictive Modeling in e-health using artificial intelligence

Predictive Modeling in e-health using artificial intelligence. Mark hoogendoorn (and many others including michel klein ). predictive modeling : W h y?. Prevention Predictive models can identify people at risk for a certain disease and facilitate preventative measures Early diagnosis

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Predictive Modeling in e-health using artificial intelligence

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  1. Predictive Modeling in e-healthusing artificial intelligence Mark hoogendoorn (and many others includingmichelklein)

  2. predictivemodeling: Why? Prevention • Predictivemodelscanidentifypeople atrisk for a certaindiseaseandfacilitatepreventativemeasures Early diagnosis • Performanearly diagnosis toincrease treatment success Highlypersonalizedinterventions • Interventionscanbetailoredbased on predictions,e.g. by providingtailored feedback, selectingappropriate treatment plans, etc. • Needvery fine-grainedmodelsforthispurpose

  3. predictivemodeling: how? Traditional in the health domain • Form a hypothesis aboutlikelypredictors • Collect data • Create a predictive model andevaluate But now…. • Overwhelming amounts of data (EMR, mobile phones, genetic data, quantified self) • How to select hypotheses and generate accurate models that utilize this wealth of information? • Use techniques from Artificial Intelligence: Data Mining • But: don’t ignore the existing body of knowledge in the field

  4. Example 1: depression Two projects: E-COMPARED and ICT4Depression ICT4Depression (EU-FP7, with VU-PSY, GGZinGeest, ….) • Develop an automated intervention using a mobile phone • Perform a lot of measurements to build up a picture of the patient • Provide feedback and therapy advice based on a predictive model • Model was based on theories from psychology E-COMPARED (EU-FP7 with similar partners) • Predict most suitable therapy and course of depression • Use data to improve the model (data mining)

  5. Example 1: depression ICT4Depression model

  6. Example 1: depression ICT4Depression prediction

  7. You can also use our mobile system The ICT4Depression system is nowavailableforyoutouse Whatcanyouuseitfor? • PerformingEcologicalMomentary Assessments (EMA) studiesvia the mobile phone in any domain • Mobile interventions (alsooutside of the depression domain) withdedicated feedback • We have a dedicatedvalorization project forthiscalledIntelliHealth • We hire computer sciencestudentstotailor the systems toyour trials • See www.intellihealth.nl

  8. Example 2: CRC Develop a predictive model forcolorectalcancer (CRC) Early diagnosis is crucial forhighsurvivalrates Together with VUMC, LUMC, UMC, and IBM Useelectronicmedical records (EMRs) from GP What information do we have? • 180,000 patientsbetween 2007 and 2011 (approx. 500 CRC patients) • General characteristics (age, gender, ….) • All consults (date visited, ICPC code of visit) • All medications prescribed (date prescribed, ATC code of medication) • All lab results (date, values of lab test) • And a lot of unstructured data (free text)

  9. Example 2: CRC Using a simple data mining approach we can already get quite a reasonable accuracy

  10. The end Questions? Mark Hoogendoorn Email: m.hoogendoorn@vu.nl URL: http://www.cs.vu.nl/~ mhoogen intellihealth.nl Tel. 020-5987772

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