ORIGINAL RESEARCH article
Front. Psychiatry
Sec. Public Mental Health
Volume 16 - 2025 | doi: 10.3389/fpsyt.2025.1474786
Predicting the hospitalization burdens of patients with mental disease: multiple model comparison
Provisionally accepted
- 1Department of Psychiatry, Huai'an No.3 People's Hospital, Huai'an, China
- 2Department of Psychiatry, Huai’an Third People’s Hospital, Huai’an, China, Huai'an, China
- 3Operations Management Department, Huai’an Third People’s Hospital, Huai’an, China, Huai'an, China
- 4Department of Computer Science, Jiangsu Vocational College of Finance and Economics, Huai’an, China, Huai'an, China
- 5Information Management Department, Huai’an Third People’s Hospital, Huai’an, China, Huai'an, China
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Background: Mental disorders represent a growing public health challenge, with rising hospitalization rates worldwide. Despite their significant impact, systematic investigations into the hospitalization burden (HB) of mental disorders remain notably lacking in current studies. Objective: This study aims to employ machine learning techniques to predict the HB among patients with mental disorders. By doing so, we seek to optimize the allocation of medical resources and enhance the efficiency of healthcare services for this specific population.Methods: Historical hospitalization data was collected, encompassing patient demographics, diagnostic details, length of stay, costs, and other relevant information. The data was then cleaned to remove missing values and outliers, and key features related to HBs were extracted. A statistical analysis of the basic characteristics of HBs was conducted. Subsequently, prediction models for HBs were developed based on the historical data and identified key features, including time series models and regression models. The predictive ability of these models was evaluated by comparing the actual values with the predicted values.Results: HB was influenced by diagnosis, age, and seasonality, with schizophrenia (A3) and personality disorders (A7) incurring the highest burdens. ML models demonstrated task-specific efficacy: RR for hospitalization frequency (HF), LSTM/CBR for Length of stay (LOS), SARIMAX/LGBMR for hospitalization costs (HC). Findings support tailored resource allocation and early intervention for high-risk groups.Conclusion: This study showcased the effectiveness of machine learning methods in predicting the hospitalization burden of inpatients with mental disorders, thereby offering scientific decision support for medical institutions. This approach contributes to enhancing the quality of patient care and optimizing the efficiency of medical resource utilization.
Keywords: mental disorder, Hospitalization burden, Prediction models, time sequence models, Regression Models
Received: 02 Aug 2024; Accepted: 26 May 2025.
Copyright: © 2025 Lu, Jing, Li, Yelin, Ziyu and Zhiguo. 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: Liu Zhiguo, Information Management Department, Huai’an Third People’s Hospital, Huai’an, China, Huai'an, China
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