Published on Thu Mar 21 2019

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

Taiyao Wang, Ioannis Ch. Paschalidis

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. We leverage a large dataset containing over 2.28million patients who had surgeries in the period 2011--2014 in the U.S.

0
0
0
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).

Thu Apr 07 2016
Machine Learning
Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values
This work is motivated by the needs of predictive analytics on healthcare data as represented by Electronic Medical Records. Such data is invariably noisy, with missing entries, with imbalance in classes of interest. Since standard data mining methods often produce poor performance measures, we argue for specialized techniques.
0
0
0
Wed Jan 03 2018
Machine Learning
Predicting Chronic Disease Hospitalizations from Electronic Health Records: An Interpretable Classification Approach
Urban living has significant adverse effects on health, increasing the risk of several chronic diseases. We focus on the two leading clusters of chronic disease, heart disease and diabetes. We develop data-driven methods to predict hospitalizations due to these conditions.
0
0
0
Wed Dec 05 2012
Machine Learning
Cost-Sensitive Support Vector Machines
A new procedure for learning cost-sensitive SVM classifiers is proposed. The SVM hinge loss is extended to the cost sensitive setting. The CS-SVM is derived as the minimizer of the associated risk.
0
0
0
Sat Dec 26 2020
Machine Learning
Explainable Multi-class Classification of Medical Data
Machine Learning applications have brought new insights into a secondary analysis of medical data. Machine Learning helps to develop new drugs, define populations susceptible to certain illnesses, identify predictors of many common diseases. Results depend on the convolution of many factors, including feature selection, class (im)balance,
0
0
0
Sat Mar 21 2015
Machine Learning
Fast Imbalanced Classification of Healthcare Data with Missing Values
In medical domain, data features often contain missing values. This can create serious bias in the predictive modeling. Typical standard data mining methods often produce poor performance measures. We propose a new method to simultaneously classify large datasets.
0
0
0
Sun Dec 06 2020
Artificial Intelligence
A Weighted Solution to SVM Actionability and Interpretability
0
0
0