Evaluating Patient Readmission Risk: A Predictive Analytics Approach

TL;DR

This paper develops a predictive model for patient readmission risk using genetic algorithms and ensemble methods, aiming to improve accuracy for clinical deployment in healthcare settings.

Contribution

It introduces a novel risk prediction model optimized with genetic algorithms and greedy ensemble techniques, enhancing predictive accuracy over existing models.

Findings

Improved readmission risk prediction accuracy

Effective optimization with genetic algorithms

Enhanced model robustness for clinical use

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.