Hospitalization Prediction From Health Care Claims Adithya Renduchintala, Benjamin Martin, & Lance Legel University of Colorado Boulder  Data Mining  Spring 2012 Adithya Renduchintala, Benjamin Martin, & Lance Legel University 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?

i = current member n = number of members p = predicted days in hospital for i a = 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 10 Support Vector Machine trained March 20 SVM features integrated April 1 Artificial Neural Network trained April 10 ANN features integrated April 20 Integrated prediction