|Thursday, February 18|
|PS1 Poster Session 1 & Opening Mixer sponsored by SAS||
Thu, Feb 18, 5:30 PM - 7:00 PM
Analysis of Survival Functions in Predicting Length of Stay in Florida Hospitals (303231)Pali Sen, University of North Florida
*Benjamin Ray Webster, University of North Florida
Keywords: Failure Time Model, Hazard Function, Cox Proportional Hazard model, Odds Ratio, Healthcare Data
Big Data is an extremely useful tool for exploring relationships among observed variables. We investigate a large amount of public data that are collected for the Agency for Health Care Administration and suggest possible predictive models to interpret its outcomes. Our data consist of every Medicare inpatient hospital discharge record related to the primary diagnoses on Acute Myocardial Infarction, Heart Failure, and Pneumonia in the state of Florida for the year 2011. Our response variable is duration of stay in days. The nature of the predictor variables is either categorical or ordinal. We use an Accelerated Failure Time model and also a Cox Proportional Hazard model for the right-censored response time and analyze related distribution functions. We interpret the effect of gender, primary diagnosis, age, indicator for respiratory charges, and severity of illness as explanatory variables and use these to rank the patients in terms of expected length of stay. We use extensive amount of visual display to substantiate the outcomes. The result includes expected instantaneous rate of change on the hazard functions of Accelerated Failure Time and Cox Proportional Hazard models.