1. PREDICTIVE MODELING IN E-HEALTH USING ARTIFICIAL INTELLIGENCE MARK HOOGENDOORN (AND MANY OTHERS INCLUDING MICHEL KLEIN) MARK HOOGENDOORN (AND MANY OTHERS INCLUDING MICHEL KLEIN)

2. PREDICTIVE MODELING: WHY? Prevention Predictive models can identify people at risk for a certain disease and facilitate preventative measures Early diagnosis Perform an early diagnosis to increase treatment success Highly personalized interventions Interventions can be tailored based on predictions, e.g. by providing tailored feedback, selecting appropriate treatment plans, etc. Need very fine-grained models for this purpose 2

3. PREDICTIVE MODELING: HOW? Traditional in the health domain 1. Form a hypothesis about likely predictors 2. Collect data 3. Create a predictive model and evaluate 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 3

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) 4

5. EXAMPLE 1: DEPRESSION 5 ICT4Depression model

6. EXAMPLE 1: DEPRESSION 6 ICT4Depression prediction

7. YOU CAN ALSO USE OUR MOBILE SYSTEM The ICT4Depression system is now available for you to use What can you use it for? Performing Ecological Momentary Assessments (EMA) studies via the mobile phone in any domain Mobile interventions (also outside of the depression domain) with dedicated feedback We have a dedicated valorization project for this called IntelliHealth We hire computer science students to tailor the systems to your trials See www.intellihealth.nl 7

8. EXAMPLE 2: CRC Develop a predictive model for colorectal cancer (CRC) Early diagnosis is crucial for high survival rates Together with VUMC, LUMC, UMC, and IBM Use electronic medical records (EMRs) from GP What information do we have? 180,000 patients between 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) 8

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

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