Decision Modelling for n00bs

Clinical decision modeling for n00bs: The absolute

minimum you NEED to know

Suranga Nath Kasthurirathne

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.

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.

Data

Decision

Decision

Data

Flaws, mistakes, Individual perspectives

Decision models

Classification

J48

Logistic Regression

Etc. etc.

Clustering

EM

Simple Kmeans

Classification Vs. Clustering

Decision models

Classification

J48

Logistic Regression

Etc. etc.

Clustering

EM

Simple Kmeans

Classification Vs. Clustering

What we wont cover

• Clustering / unsupervised approaches

• How algorithms work

• How modeling tools work

How does a human being do it?

Case study: Dr. Jones selects students for the masters program

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….

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?

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!)

Weka, R project etc.

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”

Computers have their advantages

• Feature selection

• Boosting unbalanced data

• Being very very impartial and unbiased

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

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

USE A TOOL

No, really…..

Thank you