Predicting one-year readmission of adult patients with pneumonia using machine learning approaches: A case study in the Philippines

Abstract

ABSTRACT Although several predictive models have been developed for patients with pneumonia who are at high risk of readmission, limited studies have been performed to assess pneumonia-related readmission within one year conducted in the Philippines. This study aimed to determine the factors associated with one-year readmission and to evaluate the suitability of predictive models for patients with pneumonia. A retrospective observational cohort study was conducted to evaluate pneumonia admissions in the partnered tertiary hospital from June 2017 to December 2018. Regularized logistic regression (RegLR), support vector machine (SVM), C5.0 decision tree, classification and regression tree (CART), and an artificial neural network (ANN) were used to develop the predictive models, which were evaluated using the area under the receiver operating characteristic curve (AUC). Of the 1,261 patients with pneumonia, 366 (29.02%) were hospitalized within one year after discharge. The factors associated with readmission included the initial laboratory test results and hospital utilization. The results showed that RegLR gave the best overall performance when using evaluation metrics as it resulted in the lowest misclassification cost. The findings of this study could be useful when developing strategies to reduce pneumonia readmission rates, enabling clinicians and hospitals to improve healthcare delivery for pneumonia patients.