American Journal of Engineering and Applied Sciences

Evaluating Patient Readmission Risk: A Predictive Analytics Approach

Avishek Choudhury and Dr. Christopher M. Greene

DOI : 10.3844/ajeassp.2018.1320.1331

American Journal of Engineering and Applied Sciences

Volume 11, Issue 4

Pages 1320-1331

Abstract

With the emergence of the Hospital Readmission Reduction Program of the Center for Medicare and Medicaid Services on October 1, 2012, forecasting unplanned patient readmission risk became crucial to the healthcare domain. There are tangible works in the literature emphasizing on developing readmission risk prediction models; However, the models are not accurate enough to be deployed in an actual clinical setting. Our study considers patient readmission risk as the objective for optimization and develops a useful risk prediction model to address unplanned readmissions. Furthermore, Genetic Algorithm and Greedy Ensemble is used to optimize the developed model constraints.

Copyright

© 2018 Avishek Choudhury and Dr. Christopher M. Greene. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.