Hospitalization Prediction From Health Care Claims

Adithya Renduchintala, Benjamin Martin, & Lance LegelUniversity of Colorado Boulder Data Mining Spring

2012

OVERVIEW•Why hospitalization?•What will we do?•What is our data?•How will we evaluate?•How will we research?•How will we implement?•When are our milestones?

WHY HOSPITALIZATION?

• 70 million Americans hospitalized / year• 5 million / year preventable

→ $30 billion / year•Data mining can help!

WHAT WILL WE DO?

→ →

Analyze health care data on 76,000 people over a 3 year period with 2.6 million events

Correlate events and hospitalization outcomes to train prediction algorithms

Predict number of days a person will be hospitalized next year given new event data

WHAT IS OUR DATA?

2,600,000 instances of above data for 76,000 unique members +• Member sex and age group• Number of drugs prescribed per member• Number of laboratory and pathology tests per member

WHAT IS OUR DATA?

WHAT IS OUR DATA?

WHAT IS OUR DATA?

WHAT IS OUR DATA?

i = current member n = number of membersp = predicted days in hospital for ia = actual days in hospital for i

HOW WILL WE EVALUATE?

HOW WILL WE RESEARCH?

1. “Data mining and clinical data repositories: Insights from a 667,000 patient data set”

2. “Introduction to neural networks in health care”

3. “Stock market prediction system with modular neural networks”

HOW WILL WE IMPLEMENT?

↔ ↔

Support vector machine to classify members as “yes” or “no” for being hospitalized

Feature engineering of domain model knowledge into learning algorithms

Neural network to quantify number of days “yes” members are hospitalized

WHEN ARE OUR MILESTONES?

March 1 Regression fitted

March 20 SVM features integrated

April 10 ANN features integrated