Predicting length of stay across hospital departments
Autores
Puentes Gutiérrez, Jesús Manuel; Sicilia Urbán, Miguel Ángel; Sánchez Alonso, Salvador; García Barriocanal, María ElenaIdentificadores
Enlace permanente (URI): http://hdl.handle.net/10017/50591DOI: 10.1109/ACCESS.2021.3066562
ISSN: 2169-3536
Editor
IEEE
Fecha de publicación
2021-03-17Cita bibliográfica
Puentes Gutiérrez, J.M., Sicilia, M.A., Sánchez Alonso, S. & García Barriocanal, E. 2021, "Predicting length of stay across hospital departments", IEEE Access, vol. 9, pp. 44671-44680.
Palabras clave
Length of stay
Hospital department
Machine learning
Decision tree
Random forest
Tipo de documento
info:eu-repo/semantics/article
Versión
info:eu-repo/semantics/publishedVersion
Versión del editor
https://doi.org/10.1109/ACCESS.2021.3066562Derechos
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Derechos de acceso
info:eu-repo/semantics/openAccess
Resumen
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.
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