JSM 2012 Home

JSM 2012 Online Program

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

Online Program Home

Abstract Details

Activity Number: 356
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #305885
Title: Predictive Modeling for Preventable Hospital Readmissions: A Data Mining Approach
Author(s): Jingjing Gao*+ and Xia Lin and Andrew Post and Sharath Cholleti and Joel Saltz
Companies: Emory University and Emory University and Emory University and Emory University and Emory University
Address: 1867 Ridgemont Ln, Decator, GA, 30322,
Keywords: classification and prediction ; hospital readmission rate ; decision trees ; classification and regression trees ; random forest ; support vector machine

The accelerated use of electronic health records (EHR) has allowed researcher to integrate and retrieve longitudinal and high-dimensional electronic patient data in a more efficiently way, which leads to extensive retrospective data analysis on patients' healthcare history. The hospital 30-day readmission rate is one of the arising topics attracted great attention. Previous studies constructed generalized linear models for classification and prediction, which have limited feasibility in handling remarkable quantities of EHR data mainly due to inevitable assumptions of parametric models, whereas data mining provides methods and tools to extract meaningful patterns and rules without making assumptions on underlying distribution. In this study, we apply several classification techniques including CART, Random Forest and SVM to identify leading medical and clinical factors that distinguish those patients who are highly likely to be readmitted to hospital within 30 days of initial visit from those who are not. K-fold cross-validation is used for evaluation. High accuracy has been achieved especially in classifying patients who are highly unlikely to be readmitted within 30 days.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2012 program

2012 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.