1. The Individual Patient:
the ATIA approach
Bo de Lange

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
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3. Characteristics
• Biology
– anatomy • Medicine
– Physiology
– diagnoses
– -omics data
– treatments
• Genomic
• Proteomics – …
• Metabolomics • Demographics
– …. – education
• Psychology – welfare
– personality – domestic situation
– behaviour – …
– cognition
– …
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4. Defining characteristics
Dweeb
Social
Intelligence Obsession
Nerd ineptitude
Geek Dork
Obsession
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5. Artificial Intelligence
Develop and improve agents & multi-agent systems
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6. Knowledge
Make data and knowledge available for reasoning
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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
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13. 18-06-2012 The individual patient 13

14. Moku tree: improve specificity
user defined error weighting
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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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16. Apply decision rules
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17. Truly personalised medicine
X X
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