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 Medicine
13 March 2013 2
7. Advice
Combine agents, data and knowledge into
prediction models and decision support systems
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8. AI: Multi-Agent configuration
•Decision trees (Mo)
•Bayesian classifier (Na)
•Interaction information (Le)
•Neural network (Ri)
•Rule based reasoning (Ce)
•…
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9. Knowledge representation
Challenges
• 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 values
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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
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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
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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 model
18-06-2012 The individual patient 12
14. Moku tree: improve specificity
user defined error weighting
18-06-2012 The individual patient 14
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 &
SPV
Example tree and ROC for treatment 2
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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