  Identification of patients at high risk for readmission could help reduce
morbidity and mortality as well as healthcare costs. Most of the existing
studies on readmission prediction did not compare the contribution of data
categories. In this study we analyzed relative contribution of 90,101 variables
across 398,884 admission records corresponding to 163,468 patients, including
patient demographics, historical hospitalization information, discharge
disposition, diagnoses, procedures, medications and laboratory test results. We
established an interpretable readmission prediction model based on Logistic
Regression in scikit-learn, and added the available variables to the model one
by one in order to analyze the influences of individual data categories on
readmission prediction accuracy. Diagnosis related groups (c-statistic
increment of 0.0933) and discharge disposition (c-statistic increment of
0.0269) were the strongest contributors to model accuracy. Additionally, we
also identified the top ten contributing variables in every data category.
