Mathematical Modeling and Optimal Stopping Theory-based additional layers for 30-Day Rate Risk Prediction of Readmission to Intensive Care Units

Abstract

The importance of 30-day patients’ readmissions (PRs) to intensive unit care stems from the significant cost and mortality risk when the patient’s chosen class (i.e., readmitted or not to the hospital) is incorrect. The overall accuracy (OA) of the PRs classification obtained in the literature is still moderate, particularly for machine learning (ML)-enabled ANNs, where OA is around 65%, resulting in 35% critical wrong decisions. To improve such an OA, a three-stage ML-assisted algorithm employing both support vector machines (SVMs) and artificial neural networks (ANNs) techniques is proposed. Starting with a well-fitted PR accuracies distribution and using mathematical modeling, the global optimal accuracies’ interval of misclassified patients (OIMP) is obtained, and a novel theorem for the generalized secretary problem is introduced and used to select the most likely well-classified patients. The algorithm’s final phase consists of flipping the remaining incorrectly classified patients in the OIMP. Using the presented approach with ANN and SVM, the OA was raised by 5% and 19%, respectively. The proposed approach may be used for any binary classification application and expanded to any multi-class problem.