Evaluating Patient Readmission Risk: A Predictive Analytics Approach
- 1 Syracuse University, United States
- 2 Binghamton University, United States
Copyright: © 2020 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.
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.
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- Prediction Model
- Patient Readmission Risk
- Healthcare Expenses
- Healthcare Quality
- Optimization Model