Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/89817

TítuloPredictive analytics for hospital discharge flow determination
Autor(es)Faria, Mariana
Barbosa, Agostinho
Guimarães, Tiago André Saraiva
Lopes, João
Santos, Manuel
Palavras-chaveLength of Stay
Machine learning
Predictive analytics
Data2022
EditoraElsevier 1
RevistaProcedia Computer Science
Resumo(s)In recent years, hospitals around the world are faced with large patient flows, which negatively affect the quality of patient care and become a crucial factor to consider in inpatient management. The main objective of this management is to maximize the number of available beds, using efficient planning. Intensive Care Units (ICU) are hospital units with a higher monetary consumption, and the importance of indicators that allow the achievement of useful information for a correct management is critical. This study allowed the prediction of the Length of Stay (LOS) based on their demographic data, information collected at the time of admission and clinical conditions, which can help health professionals in conducting a more assertive planning and a better quality service. The results obtained show that Machine Learning (ML) models, using demographic information simultaneously with the patient's pathway, as well as clinical data, drugs, tests and analysis, introduce a greater predictive ability for LOS.
TipoArtigo em ata de conferência
URIhttps://hdl.handle.net/1822/89817
DOI10.1016/j.procs.2022.10.145
Arbitragem científicayes
AcessoAcesso aberto
Aparece nas coleções:CAlg - Artigos em livros de atas/Papers in proceedings

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