From Predictions to Decisions

Bringing Decision Theory to Healthcare Data Science

Transcript

1. From Predictions to
   Decisions
   Bringing Decision Theory to Healthcare Data Science
   Corey Chivers, PhD
   bayesianbiologist.com
   @cjbayesian
   

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3. Is this a good model?
   (1 – Specificity)
   (Sensitivity)
   

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4. Is this a good model?
   FP
   FN TP
   TN
   

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5. "Good" is only meaningful relative to
   the "goodness" of the events being
   predicted and our ability to do
   something to change them.
   

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6. All models are
   wrong, but some
   are useful.
   - George Box
   

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7. FP
   FN TP
   TN
   

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8. How good tho?
   How good tho?
   How bad tho?
   How bad tho?
   FP
   FN TP
   TN
   

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9. Goodness can be measured in any units
   More adorbs Less adorbs
   

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10. The best decision is the one that leads to the
    most goodness
    More adorbs Less adorbs
    

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11. When outcomes are uncertain, the best decision is
    the one that has the highest expected goodness.
    

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12. When outcomes are uncertain, the best decision is
    the one that has the highest expected goodness.
    Machine Learning
    can only help you
    with this part!
    

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13. When outcomes are uncertain, the best decision is
    the one that has the highest expected goodness.
    Machine Learning
    can only help you
    with this part!
    

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14. Healthcare Example
    Predicted Sepsis
    Treated a true case
    (Potential to avoid
    bad outcome)
    Predicted Sepsis
    Treated a false case
    (unnecessary)
    Predicted No Sepsis
    Didn’t treat
    (all good)
    Predicted No Sepsis
    Failed to treat
    (Bad outcome)
    

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15. Healthcare Example
    

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16. Healthcare Example
    

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17. Healthcare Example
    • But everything just adds to the ‘badness’ total.
    • How does that help us decide?
    • The key is to compare against the alternatives.
    • Treat everyone? <==> set decision threshold to zero
    • Treat no one? <==> set decision threshold to one
    • Some other strategy? <==> Random? Status quo?
    • Choose the alternative that is least bad
    • Or, if you prefer the J perspective, multiply everything by -1 and choose the
    one that’s the most good!
    

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18. Healthcare Example
    • But everything just adds to the ‘badness’ total.
    • How does that help us decide?
    • The key is to compare against the alternatives.
    • Treat everyone? <==> set decision threshold to zero
    • Treat no one? <==> set decision threshold to one
    • Some other strategy? <==> Random? Status quo?
    • Choose the alternative that is least bad
    • Or, if you prefer the J perspective, multiply everything by -1 and choose the
    one that’s the most good!
    

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19. So, like, should we use this model or what?
    • There are still things we don’t know,
    like what all these C terms actually are.
    • Work backwards and ask:
    • In what range would they need to be for
    us to choose the model over an
    alternative strategy?
    • How much better would the model need
    to be before we would choose to use it?
    

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20. Treat none
    Cost of Intervention ($)
    Cost of event ($)
    Treat all
    Pregnancy Related Hypertension (PRH) is the leading
    cause of maternal morbidity and mortality in the U.S.
    High-risk patients à remote blood pressure monitoring
    https://healthcareinnovation.upenn.edu/projects/heart-safe-motherhood
    

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21. Assume, evaluate, refine, repeat
    • Decision theory requires making assumptions, as we’ve done here
    • Nothing is stopping us from making different assumptions:
    • “What if the false positives lead to additional harm?”
    • “What if I have other priorities, like fairness?”
    • Decision theory analysis is not infallible, but it allows us to:
    • Get the best possible answers
    • to the most precisely formulated questions,
    • when starting from a given set of assumptions
    

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22. Thanks!
    bayesianbiologist.com
    @cjbayesian
    

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