The application of machine learning algorithms for predicting length of stay before and during the COVID-19 pandemic: evidence from Wuhan-area hospitals
ObjectiveThe COVID-19 pandemic has placed unprecedented strain on healthcare systems, mainly due to the highly variable and challenging to predict patient length of stay (LOS). This study aims to identify the primary factors impacting LOS for patients before and during the COVID-19 pandemic.MethodsT...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Digital Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2024.1506071/full |
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| author | Yang Liu Yang Liu Renzhao Liang Chengzhi Zhang |
| author_facet | Yang Liu Yang Liu Renzhao Liang Chengzhi Zhang |
| author_sort | Yang Liu |
| collection | DOAJ |
| description | ObjectiveThe COVID-19 pandemic has placed unprecedented strain on healthcare systems, mainly due to the highly variable and challenging to predict patient length of stay (LOS). This study aims to identify the primary factors impacting LOS for patients before and during the COVID-19 pandemic.MethodsThis study collected electronic medical record data from Zhongnan Hospital of Wuhan University. We employed six machine learning algorithms to predict the probability of LOS.ResultsAfter implementing variable selection, we identified 35 variables affecting the LOS for COVID-19 patients to establish the model. The top three predictive factors were out-of-pocket amount, medical insurance, and admission deplanement. The experiments conducted showed that XGBoost (XGB) achieved the best performance. The MAE, RMSE, and MAPE errors before and during the COVID-19 pandemic are lower than 3% on average for household registration in Wuhan and non-household registration in Wuhan.ConclusionsResearch finds machine learning is reasonable in predicting LOS before and during the COVID-19 pandemic. This study offers valuable guidance to hospital administrators for planning resource allocation strategies that can effectively meet the demand. Consequently, these insights contribute to improved quality of care and wiser utilization of scarce resources. |
| format | Article |
| id | doaj-art-06a9eedbee8f46d8b6ddd78ce8b06fdb |
| institution | OA Journals |
| issn | 2673-253X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Digital Health |
| spelling | doaj-art-06a9eedbee8f46d8b6ddd78ce8b06fdb2025-08-20T02:38:10ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2024-12-01610.3389/fdgth.2024.15060711506071The application of machine learning algorithms for predicting length of stay before and during the COVID-19 pandemic: evidence from Wuhan-area hospitalsYang Liu0Yang Liu1Renzhao Liang2Chengzhi Zhang3School of Information Management, Wuhan University, Wuhan, ChinaShenzhen Research Institute, Wuhan University, Shenzhen, ChinaSchool of Physics and Technology, Wuhan University, Wuhan, ChinaDepartment of Information Management, Nanjing University of Science & Technology, Nanjing, ChinaObjectiveThe COVID-19 pandemic has placed unprecedented strain on healthcare systems, mainly due to the highly variable and challenging to predict patient length of stay (LOS). This study aims to identify the primary factors impacting LOS for patients before and during the COVID-19 pandemic.MethodsThis study collected electronic medical record data from Zhongnan Hospital of Wuhan University. We employed six machine learning algorithms to predict the probability of LOS.ResultsAfter implementing variable selection, we identified 35 variables affecting the LOS for COVID-19 patients to establish the model. The top three predictive factors were out-of-pocket amount, medical insurance, and admission deplanement. The experiments conducted showed that XGBoost (XGB) achieved the best performance. The MAE, RMSE, and MAPE errors before and during the COVID-19 pandemic are lower than 3% on average for household registration in Wuhan and non-household registration in Wuhan.ConclusionsResearch finds machine learning is reasonable in predicting LOS before and during the COVID-19 pandemic. This study offers valuable guidance to hospital administrators for planning resource allocation strategies that can effectively meet the demand. Consequently, these insights contribute to improved quality of care and wiser utilization of scarce resources.https://www.frontiersin.org/articles/10.3389/fdgth.2024.1506071/fulllength of stayCOVID-19 pandemicmachine leaningmedical insurancehousehold registration |
| spellingShingle | Yang Liu Yang Liu Renzhao Liang Chengzhi Zhang The application of machine learning algorithms for predicting length of stay before and during the COVID-19 pandemic: evidence from Wuhan-area hospitals Frontiers in Digital Health length of stay COVID-19 pandemic machine leaning medical insurance household registration |
| title | The application of machine learning algorithms for predicting length of stay before and during the COVID-19 pandemic: evidence from Wuhan-area hospitals |
| title_full | The application of machine learning algorithms for predicting length of stay before and during the COVID-19 pandemic: evidence from Wuhan-area hospitals |
| title_fullStr | The application of machine learning algorithms for predicting length of stay before and during the COVID-19 pandemic: evidence from Wuhan-area hospitals |
| title_full_unstemmed | The application of machine learning algorithms for predicting length of stay before and during the COVID-19 pandemic: evidence from Wuhan-area hospitals |
| title_short | The application of machine learning algorithms for predicting length of stay before and during the COVID-19 pandemic: evidence from Wuhan-area hospitals |
| title_sort | application of machine learning algorithms for predicting length of stay before and during the covid 19 pandemic evidence from wuhan area hospitals |
| topic | length of stay COVID-19 pandemic machine leaning medical insurance household registration |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2024.1506071/full |
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