Predicting the hospitalization burdens of patients with mental disease: a multiple model comparison.

Abstract

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 (ML) 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 were collected, encompassing patient demographics, diagnostic details, length of stay, costs, and other relevant information. The data were then cleaned to remove missing values and outliers, and key features related to the HB were extracted. A statistical analysis of the basic characteristics of the HB was conducted. Subsequently, prediction models for the HB 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: ridge regression for hospitalization frequency, long short-term memory/categorical boosting regression for length of stay, and seasonal autoregressive integrated moving average with exogenous regressors/light gradient boosting machine regression for hospitalization costs. The 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.