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ORIGINAL RESEARCH article

Front. Digit. Health
Sec. Connected Health
Volume 6 - 2024 | doi: 10.3389/fdgth.2024.1506071
This article is part of the Research Topic Digital Health Innovations for Patient-Centered Care View all 6 articles

The application of machine learning algorithms for predicting length of stay before and during the COVID-19 pandemic: Evidence from Wuhan-area hospitals

Provisionally accepted
Yang Liu Yang Liu 1*Renzhao Liang Renzhao Liang 1Chengzhi Zhang Chengzhi Zhang 2
  • 1 Wuhan University, Wuhan, China
  • 2 Nanjing University of Science and Technology, Nanjing, Jiangsu Province, China

The final, formatted version of the article will be published soon.

    The 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.Methods: This study collected electronic medical record data from Zhongnan Hospital of Wuhan University. We employed six machine learning algorithms to predict the probability of LOS.: After 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.Research 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.

    Keywords: Length of Stay, COVID-19 pandemic, Machine leaning, Medical insurance, Household registration

    Received: 04 Oct 2024; Accepted: 03 Dec 2024.

    Copyright: © 2024 Liu, Liang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yang Liu, Wuhan University, Wuhan, China

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