Prescriptive Cluster-Dependent Support Vector Machines with an Application to Reducing Hospital Readmissions

TL;DR

This paper enhances linear SVM classifiers with sparsity, cluster-based classification, and optimization of controllable variables to provide personalized recommendations, demonstrated on a large dataset for reducing hospital readmissions.

Contribution

It introduces a novel cluster-dependent sparse SVM framework with optimization for personalized decision-making in healthcare.

Findings

Effective reduction in hospital readmissions predicted by the model.

Personalized prescriptions improve patient outcomes.

Scalability demonstrated on large US surgical dataset.

Abstract

We augment linear Support Vector Machine (SVM) classifiers by adding three important features: (i) we introduce a regularization constraint to induce a sparse classifier; (ii) we devise a method that partitions the positive class into clusters and selects a sparse SVM classifier for each cluster; and (iii) we develop a method to optimize the values of controllable variables in order to reduce the number of data points which are predicted to have an undesirable outcome, which, in our setting, coincides with being in the positive class. The latter feature leads to personalized prescriptions/recommendations. We apply our methods to the problem of predicting and preventing hospital readmissions within 30-days from discharge for patients that underwent a general surgical procedure. To that end, we leverage a large dataset containing over 2.28 million patients who had surgeries in the period 2011--2014 in the U.S. The dataset has been collected as part of the American College of Surgeons National Surgical Quality Improvement Program (NSQIP).