Predicting hospital and emergency department utilization among community-dwelling older adults: Statistical and machine learning approaches

Aaron Jones, Andrew P. Costa, Angelina Pesevski, Paul D. McNicholas, Dongmei Li
2018 PLoS ONE  
Objective The objective of this study was to compare the performance of several commonly used machine learning methods to traditional statistical methods for predicting emergency department and hospital utilization among patients receiving publicly-funded home care services. Study design and setting We conducted a population-based retrospective cohort study of publicly-funded home care recipients in the Hamilton-Niagara-Haldimand-Brant region of southern Ontario, Canada between 2014 and 2016.
more » ... en 2014 and 2016. Gradient boosted trees, neural networks, and random forests were tested against two variations of logistic regression for predicting three outcomes related to emergency department and hospital utilization within six months of a comprehensive home care clinical assessment. Models were trained on data from years 2014 and 2015 and tested on data from 2016. Performance was compared using logarithmic score, Brier score, AUC, and diagnostic accuracy measures.
doi:10.1371/journal.pone.0206662 fatcat:le3rlx7gpfbuxh3xmvu35vgqsm