Bo de Lange

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  • Uitdaging:Wat wil je voorspellen en wat zijn je inputvariabelenDe eigenschappen van de patiëntMeerwaarde: kennis van het vakgebiedDiscretiserenHet aantal categorieën bij bijvoorbeeld diagnoses comorbiditeitenDe keuzes van de categorie grenzen (hard-fuzzy, handmatig-automatisch, width-size)Hoe om te gaan met missing values: het schrappen van de variabele allochtonie
  • Op basis van ALLE BESCHIKBAREindividueleeigenschappen van de patient (groen)Met regels uit de algoritmen(evtaangevuld met kennisuitboeken en richtlijnen)Advies of conclusiesafleiden (blauw)Compleet en snelTransparantBeterpresterendemodellen
  • Bo de Lange

    1. 1. The Individual Patient: the ATIA approach Bo de Lange
    2. 2. How does the Individual benefit from artificial intelligence? The individual patient• Find and Exploit differences and similarities• Which combination(s) of characteristics can predict: – Diagnosis – Treatment efficacy – Adverse events Personalised Medicine13 March 2013 2
    3. 3. Characteristics • Biology – anatomy • Medicine – Physiology – diagnoses – -omics data – treatments • Genomic • Proteomics – … • Metabolomics • Demographics – …. – education • Psychology – welfare – personality – domestic situation – behaviour – … – cognition – …13 March 2013 3
    4. 4. Defining characteristics Dweeb SocialIntelligence Obsession Nerd ineptitude Geek Dork Obsession13 March 2013 4
    5. 5. Artificial IntelligenceDevelop and improve agents & multi-agent systems13 March 2013 5
    6. 6. KnowledgeMake data and knowledge available for reasoning13 March 2013 6
    7. 7. AdviceCombine agents, data and knowledge intoprediction models and decision support systems13 March 2013 7
    8. 8. AI: Multi-Agent configuration •Decision trees (Mo) •Bayesian classifier (Na) •Interaction information (Le) •Neural network (Ri) •Rule based reasoning (Ce) •…13 March 2013 8
    9. 9. Knowledge representationChallenges• Define input and output variables • Characteristics or conditions • Domain experts knowledgeable about the field • May be used in If -> Then rules• Discretization • Number of categories • K-means, equal width, equal size • Distribution (representation in each cell)• Missing values13 March 2013 9
    10. 10. Lenny: variable selection• Claude Shannon’s information theory• Mutual Information • Information shared between input and output variables• Interaction Information • Synergy: positive interaction information (2+2=5) • Redundancy: negative interaction information (2+2=3)• Non-linear 13 March 2013 10
    11. 11. Examples application Lenny• Find clusters of voxels in 20,000 voxels in structural MRI brain scans of 400 people that share information with certain characterics (e.g. sex).• Analyses of some 3,600 mutations in HIV virus in 13,000 Treatment Change Episodes 13 March 2013 11
    12. 12. Moku: decision trees• Decision tree generating algorithm – Train set and validation set (70%-30%) – Node selection based on mutual information – Categorical Data – Extensive Tree Performance settings – Confusion matrix with user defined error weighting – Best (n) trees from a forest of trees – Combine trees in prognostic model18-06-2012 The individual patient 12
    13. 13. 18-06-2012 The individual patient 13
    14. 14. Moku tree: improve specificity user defined error weighting 18-06-2012 The individual patient 14
    15. 15. Treatment advice depression • 10 best decision trees per treatment • Classify all cases using all trees • Compare predicted with actual treatment – Step(s) up, same, step(s) down % Succesfull response Client ATIA Psychotherapy 58 68 Psychotherapy & 58 71 Medication Psychotherapy & 53 72 Medication & SPVExample tree and ROC for treatment 2 13 March 2013 15
    16. 16. Apply decision rules13 March 2013 16
    17. 17. Truly personalised medicine X X13 March 2013 17

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