Abstract
Emergency Departments (ED) in hospitals around the world are a critical service in medical care of patients. Being able to predict the number of patients every day, week or month could be of great help to the healthcare system, and especially in the current situation.
In this paper we present some results in forecasting the admissions, the inpatients and the discharges series in EDs by using Machine Learning algorithms. We have considered different time aggregations (specifically: eight hours, twelve hours, one day and the official shifts of the workers) and exogenous variables (in particular, the Spanish public holiday calendar, the academic calendar, the phase of the lunar cycle and national football matches).
Results show that the best performance is obtained for the admissions series using eight hours aggregations and the biggest improvement is with the daily aggregation in the admission series. The academic calendar and public holidays were the most selected variables.
First author is supported by Consejería de Ciencia, Universidades e Innovación of Comunidad de Madrid (PEJ-2020-AI/TIC-19375). This study was funded by Junta de Comunidades de Castilla-La Mancha (SBPLY/19/180501/000024) and Ministerio de Ciencia e Innovación (PID2019-109891RB-I00).
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Acknowledgements
The authors would like to acknowledge the clinical support of Mr. Francisco López Martínez, Mrs. María Isabel Pascual Benito and Mrs. Helena Hernández Martínez. Also, the authors want to thank the support of the Hospital Universitario de Guadalajara (HUGU), Spain.
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Álvarez-Chaves, H., Barrero, D.F., Cobos, M., R-Moreno, M.D. (2021). Patients Forecasting in Emergency Services by Using Machine Learning and Exogenous Variables. In: Bramer, M., Ellis, R. (eds) Artificial Intelligence XXXVIII. SGAI-AI 2021. Lecture Notes in Computer Science(), vol 13101. Springer, Cham. https://doi.org/10.1007/978-3-030-91100-3_15
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DOI: https://doi.org/10.1007/978-3-030-91100-3_15
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