Predicting Hospital Re-admissions from Nursing Care Data of Hospitalized Patients

TL;DR

This paper presents a novel approach using association mining for dimension reduction in high-dimensional EHR data to accurately predict hospital readmissions, aiming to improve patient care and reduce healthcare costs.

Contribution

It introduces the use of association mining for dimension reduction in EHR data to enhance readmission prediction models, addressing non-linear variable challenges.

Findings

Models achieve significantly accurate re-admission predictions

Association mining effectively reduces data dimensionality

Potential to improve treatment strategies and reduce costs

Abstract

Readmission rates in the hospitals are increasingly being used as a benchmark to determine the quality of healthcare delivery to hospitalized patients. Around three-fourths of all hospital re-admissions can be avoided, saving billions of dollars. Many hospitals have now deployed electronic health record (EHR) systems that can be used to study issues that trigger readmission. However, most of the EHRs are high dimen-sional and sparsely populated, and analyzing such data sets is a Big Data challenge. The effect of some of the well-known dimension reduction techniques is minimized due to presence of non-linear variables. We use association mining as a dimension reduction method and the results are used to develop models, using data from an existing nursing EHR system, for predicting risk of re-admission to the hospitals. These models can help in determining effective treatments for patients to minimize the possibility of re-admission, bringing down the cost and increasing the quality of care provided to the patients. Results from the models show significantly accurate predictions of patient re-admission.