Decision Modelling for n00bs
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This document provides an introduction to clinical decision modeling. It defines a decision model as an intellectual template for organizing business logic behind decisions. Decision models allow leveraging existing information, save time, and enable computers to perform complicated analysis. The document discusses using classification and clustering algorithms in decision modeling and compares how humans and computers approach decision making. It notes some challenges in decision modeling like overfitting, imbalance, and missing data, but emphasizes computers' advantages in feature selection and impartiality. The document encourages knowing the data well, using a gold standard outcome variable, employing existing modeling tools, and involving statisticians.
![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.](https://image.slidesharecdn.com/bovispqgtcae0rhrxqoq-signature-7400fe948b877ea349d2a8733d190f873e28c76369d91942296bc930295b68c9-poli-151125202851-lva1-app6891/85/Decision-Modelling-for-n00bs-2-320.jpg)
Decision Modelling for n00bs
- 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.
- 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
Editor's Notes
- #2: Two points: 1. lot of interdisciplinary students who don’t understand what decision modelling is (2) Want vs. need, economics
- #3: What is a Decision Model? If so and so, then you make a decision
- #11: What in red? Mistake that are secific to humans
- #14: In statistics and machine learning, overfitting occurs when a statistical model describes random error or noise instead of the underlying relationship.