Thirty-day hospital readmission prediction model based on common data model with weather and air quality data

Abstract Although several studies have attempted to develop a model for predicting 30-day re-hospitalization, few attempts have been made for sufficient verification and multi-center expansion for clinical use. In this study, we developed a model that predicts unplanned hospital readmission within 3...

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Autores principales:	Borim Ryu, Sooyoung Yoo, Seok Kim, Jinwook Choi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9514bcd60f704efc8aecd9f8b3b439ac
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spelling	oai:doaj.org-article:9514bcd60f704efc8aecd9f8b3b439ac2021-12-05T12:11:22ZThirty-day hospital readmission prediction model based on common data model with weather and air quality data10.1038/s41598-021-02395-92045-2322https://doaj.org/article/9514bcd60f704efc8aecd9f8b3b439ac2021-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02395-9https://doaj.org/toc/2045-2322Abstract Although several studies have attempted to develop a model for predicting 30-day re-hospitalization, few attempts have been made for sufficient verification and multi-center expansion for clinical use. In this study, we developed a model that predicts unplanned hospital readmission within 30 days of discharge; the model is based on a common data model and considers weather and air quality factors, and can be easily extended to multiple hospitals. We developed and compared four tree-based machine learning methods: decision tree, random forest, AdaBoost, and gradient boosting machine (GBM). Above all, GBM showed the highest AUC performance of 75.1 in the clinical model, while the clinical and W-score model showed the best performance of 73.9 for musculoskeletal diseases. Further, PM10, rainfall, and maximum temperature were the weather and air quality variables that most impacted the model. In addition, external validation has confirmed that the model based on weather and air quality factors has transportability to adapt to other hospital systems.Borim RyuSooyoung YooSeok KimJinwook ChoiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Borim Ryu Sooyoung Yoo Seok Kim Jinwook Choi Thirty-day hospital readmission prediction model based on common data model with weather and air quality data
description	Abstract Although several studies have attempted to develop a model for predicting 30-day re-hospitalization, few attempts have been made for sufficient verification and multi-center expansion for clinical use. In this study, we developed a model that predicts unplanned hospital readmission within 30 days of discharge; the model is based on a common data model and considers weather and air quality factors, and can be easily extended to multiple hospitals. We developed and compared four tree-based machine learning methods: decision tree, random forest, AdaBoost, and gradient boosting machine (GBM). Above all, GBM showed the highest AUC performance of 75.1 in the clinical model, while the clinical and W-score model showed the best performance of 73.9 for musculoskeletal diseases. Further, PM10, rainfall, and maximum temperature were the weather and air quality variables that most impacted the model. In addition, external validation has confirmed that the model based on weather and air quality factors has transportability to adapt to other hospital systems.
format	article
author	Borim Ryu Sooyoung Yoo Seok Kim Jinwook Choi
author_facet	Borim Ryu Sooyoung Yoo Seok Kim Jinwook Choi
author_sort	Borim Ryu
title	Thirty-day hospital readmission prediction model based on common data model with weather and air quality data
title_short	Thirty-day hospital readmission prediction model based on common data model with weather and air quality data
title_full	Thirty-day hospital readmission prediction model based on common data model with weather and air quality data
title_fullStr	Thirty-day hospital readmission prediction model based on common data model with weather and air quality data
title_full_unstemmed	Thirty-day hospital readmission prediction model based on common data model with weather and air quality data
title_sort	thirty-day hospital readmission prediction model based on common data model with weather and air quality data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9514bcd60f704efc8aecd9f8b3b439ac
work_keys_str_mv	AT borimryu thirtydayhospitalreadmissionpredictionmodelbasedoncommondatamodelwithweatherandairqualitydata AT sooyoungyoo thirtydayhospitalreadmissionpredictionmodelbasedoncommondatamodelwithweatherandairqualitydata AT seokkim thirtydayhospitalreadmissionpredictionmodelbasedoncommondatamodelwithweatherandairqualitydata AT jinwookchoi thirtydayhospitalreadmissionpredictionmodelbasedoncommondatamodelwithweatherandairqualitydata
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Thirty-day hospital readmission prediction model based on common data model with weather and air quality data

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