RT info:eu-repo/semantics/article
T1 Predicting length of stay across hospital departments
A1 Puentes Gutiérrez, Jesús Manuel
A1 Sicilia Urbán, Miguel Ángel
A1 Sánchez Alonso, Salvador
A1 García Barriocanal, María Elena
K1 Length of stay
K1 Hospital department
K1 Machine learning
K1 Decision tree
K1 Random forest
K1 Informática
K1 Computer science
AB The length of hospital stay and its implications have a significant economic and human impact. As a consequence, the prediction of that key parameter has been subject to previous research in recent years. Most previous work has analysed length of stay in particular hospital departments within specific study groups, which has resulted in successful prediction rates, but only occasionally reporting predictive patterns. In this work we report a predictive model for length of stay (LOS) together with a study of trends and patterns that support a better understanding on how LOS varies across different hospital departments and specialties. We also analyse in which hospital departments the prediction of LOS from patient data is more insightful. After estimating predictions rates, several patterns were found; those patterns allowed, for instance, to determine how to increase prediction accuracy in women admitted to the emergency room for enteritis problems. Overall, concerning these recognised patterns, the results are up to 21.61% better than the results with baseline machine learning algorithms in terms of error rate calculation, and up to 23.83% in terms of success rate in the number of predicted which is useful to guide the decision on where to focus attention in predicting LOS.
PB IEEE
SN 2169-3536
YR 2021
FD 2021-03-17
LK http://hdl.handle.net/10017/50591
UL http://hdl.handle.net/10017/50591
LA eng
DS MINDS@UW
RD 23-abr-2024