Observational (n=69,984)
Yes
An optimized Gradient Boosting Method can accurately predict 90-day unplanned patient readmissions, potentially aiding in targeted post-discharge interventions.
Should not yet change readmission prediction practice; leaves open prospective validation of gradient boosting models.
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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Choudhury et al. (2018) conducted an observational in Hospital readmission (n=69,984). Gradient Boosting Method vs. Other machine learning models (GLM, SVM, Rpart) was evaluated on Prediction accuracy for 90-day readmission.
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