Predicting elderly patient behaviour in rural healthcare using machine learning

Abstract

The digitization of modern health care data in a rural community has produced a vast amount of patient data stored in health care record systems. Together with the rise of computing power this data could produce effective insight through advanced analysis of this data and include it in medical applications for use in daily operations. This is the case in which structured, semi-structured and unstructured dataset from emergency room admissions is used for machine learning, in order to develop models that predict the possibility of an elderly patient returning to an emergency room within 96 hours. Logistic regression was the selected algorithm since it commonly used in the healthcare data set. The results from the model had a recall of 73% and a precision of 78%. This paper discusses the implementation of such a model in daily operations with a new approach to cost benefits. In other instances, the study is a proof of the concept of predictive modeling in a health care context in rural communities. � 2020 CEUR-WS. All rights reserved.