Predicting length of stay in hospitals intensive care unit using general admission features

According to the World Health Organization (WHO), patient Length of Stay (LOS) in hospitals is an important performance measurement and monitoring indicator. Prolonged LOS in the Intensive Care Unit (ICU) may lead to consuming hospital resources, manpower, and equipment. Therefore, accurate predicti...

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Autores principales:	Merhan A. Abd-Elrazek, Ahmed A. Eltahawi, Mohamed H. Abd Elaziz, Mohamed N. Abd-Elwhab
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Length of stay Machine learning Bias variance tradeoff Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/8875902bb08843d39dd435135103f26c
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spelling	oai:doaj.org-article:8875902bb08843d39dd435135103f26c2021-11-22T04:20:50ZPredicting length of stay in hospitals intensive care unit using general admission features2090-447910.1016/j.asej.2021.02.018https://doaj.org/article/8875902bb08843d39dd435135103f26c2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2090447921001349https://doaj.org/toc/2090-4479According to the World Health Organization (WHO), patient Length of Stay (LOS) in hospitals is an important performance measurement and monitoring indicator. Prolonged LOS in the Intensive Care Unit (ICU) may lead to consuming hospital resources, manpower, and equipment. Therefore, accurate prediction of patient LOS may aid the healthcare specialists to take medical decisions and allocate medical team and resources. As well, the patient and insurance companies may use this prediction to manage their budget. In this paper, a framework for predicting patient LOS in the ICU using different machine learning (ML) techniques is proposed. Unlike most of the previous studies, this study relies on general medical features collected on patient admission regardless of the patient diagnosis. This provide a broad scope and cover all patients making this approach general and easy to use. The prediction accuracy of the proposed approach was recorded to be very high and different for each ML technique. For example, the best prediction accuracy was achieved by fuzzy with accuracy reach 92%, while classification tree managed to achieve a prediction accuracy of 90% coming in the second place.Merhan A. Abd-ElrazekAhmed A. EltahawiMohamed H. Abd ElazizMohamed N. Abd-ElwhabElsevierarticleLength of stayMachine learningBias variance tradeoffEngineering (General). Civil engineering (General)TA1-2040ENAin Shams Engineering Journal, Vol 12, Iss 4, Pp 3691-3702 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Length of stay Machine learning Bias variance tradeoff Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Length of stay Machine learning Bias variance tradeoff Engineering (General). Civil engineering (General) TA1-2040 Merhan A. Abd-Elrazek Ahmed A. Eltahawi Mohamed H. Abd Elaziz Mohamed N. Abd-Elwhab Predicting length of stay in hospitals intensive care unit using general admission features
description	According to the World Health Organization (WHO), patient Length of Stay (LOS) in hospitals is an important performance measurement and monitoring indicator. Prolonged LOS in the Intensive Care Unit (ICU) may lead to consuming hospital resources, manpower, and equipment. Therefore, accurate prediction of patient LOS may aid the healthcare specialists to take medical decisions and allocate medical team and resources. As well, the patient and insurance companies may use this prediction to manage their budget. In this paper, a framework for predicting patient LOS in the ICU using different machine learning (ML) techniques is proposed. Unlike most of the previous studies, this study relies on general medical features collected on patient admission regardless of the patient diagnosis. This provide a broad scope and cover all patients making this approach general and easy to use. The prediction accuracy of the proposed approach was recorded to be very high and different for each ML technique. For example, the best prediction accuracy was achieved by fuzzy with accuracy reach 92%, while classification tree managed to achieve a prediction accuracy of 90% coming in the second place.
format	article
author	Merhan A. Abd-Elrazek Ahmed A. Eltahawi Mohamed H. Abd Elaziz Mohamed N. Abd-Elwhab
author_facet	Merhan A. Abd-Elrazek Ahmed A. Eltahawi Mohamed H. Abd Elaziz Mohamed N. Abd-Elwhab
author_sort	Merhan A. Abd-Elrazek
title	Predicting length of stay in hospitals intensive care unit using general admission features
title_short	Predicting length of stay in hospitals intensive care unit using general admission features
title_full	Predicting length of stay in hospitals intensive care unit using general admission features
title_fullStr	Predicting length of stay in hospitals intensive care unit using general admission features
title_full_unstemmed	Predicting length of stay in hospitals intensive care unit using general admission features
title_sort	predicting length of stay in hospitals intensive care unit using general admission features
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/8875902bb08843d39dd435135103f26c
work_keys_str_mv	AT merhanaabdelrazek predictinglengthofstayinhospitalsintensivecareunitusinggeneraladmissionfeatures AT ahmedaeltahawi predictinglengthofstayinhospitalsintensivecareunitusinggeneraladmissionfeatures AT mohamedhabdelaziz predictinglengthofstayinhospitalsintensivecareunitusinggeneraladmissionfeatures AT mohamednabdelwhab predictinglengthofstayinhospitalsintensivecareunitusinggeneraladmissionfeatures
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Predicting length of stay in hospitals intensive care unit using general admission features

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