A basic intro to decision modeling for non techies. View and be edumacated :)

1. Clinical decision modeling for
n00bs: The absolute
minimum you NEED to know
Suranga Nath Kasthurirathne

2. What is a Decision Model?
An intellectual template for perceiving,
organizing, and managing the business logic
behind a business decision [1].
When applied to Computer science,
and explained in plain English,
A computer based system that predicts an
outcome or makes a decision
[1] Gottinger, H. W., & Weimann, P. (1992). Intelligent decision support systems. Decision Support
Systems, 8(4), 317-332.

3. Why do we care?
Decision models:
• Allow us to leverage existing silos of
information for valuable purposes
• Saves time
• Lets the computer (and not a human) do
extremely complicated thinking
• Is very cool and science-y.

4. Data
Decision
Decision
Data
Flaws, mistakes,
Individual perspectives

5. Decision
models
Classification
J48
Logistic
Regression
Etc. etc.
Clustering
EM
Simple
Kmeans
Classification Vs. Clustering

6. Decision
models
Classification
J48
Logistic
Regression
Etc. etc.
Clustering
EM
Simple
Kmeans
Classification Vs. Clustering

7. What we wont cover
• Clustering / unsupervised approaches
• How algorithms work
• How modeling tools work

8. How does a human being do it?
Case study: Dr. Jones selects students
for the masters program

9. What happens inside the reviewers
brain?
Criteria 1 Criteria 2 Criteria 3 Outcome
Mark TRUE FALSE FALSE Reject
Alex TRUE TRUE FALSE Accept
Sarah TRUE TRUE TRUE Accept
The reviewers brain is trained to watch out for
specific criteria.
So basically….

10. What can Dr. Jones do wrong?
• Selects wrong criteria
• Places emphasis on the wrong criteria
• Forgets
• Makes mistakes
• And what if Dr. Jones is doing this for the first
time?

11. How does a computer replicate this?
The computer;
• Obtains a data vector, just like our brain does
• ‘Trains’ itself on this vector (just like our brains
do)
• Delivers decisions based on this knowledge
(just like we do!)

12. Weka, R project etc.

13. Computers also share our weakness
• Over fitting
– An applicant has won 10 Nobel prizes
• Imbalance
– Applications for a postdoc position are mistakenly
sent to Dr. Jones
• Missing data
– A blank doesn’t mean zero. It means, “I don’t
know”

14. Computers have their advantages
• Feature selection
• Boosting unbalanced data
• Being very very impartial and unbiased

15. Since real life is always more
complicated…
• No binary outcome
– Accept, reject, waitlist, no scholarship, small
scholarship
• No binary criteria
– Excellent track record
– Meh track record
– Slightly impressed track record
– Incomplete

16. Performing decision modeling:
guidelines
• Know thyself, know thy data
• Have an ‘unquestionable’ gold standard
(outcome variable)
• Use an existing tool
• Get a statistician involved, EARLY

17. USE A TOOL
No, really…..

18. Thank you