Evaluating Patient Readmission Risk: A Predictive Analytics Approach
This addresses the need for more accurate readmission risk prediction models in healthcare, but it appears incremental as it builds on existing literature without claiming a breakthrough.
The study tackled the problem of predicting unplanned patient readmission risk by developing a predictive model optimized with a Genetic Algorithm and Greedy Ensemble, but no concrete accuracy numbers or results were provided in the 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.