Machine-learning-based cost prediction models for inpatients with mental disorders in China

作者：Yuxuan Ma, Xi Tu, Xiaodong Luo, Linlin Hu, Chen Wang · 发表于：BMC Psychiatry · 年份：2025 · DOI：10.1186/s12888-024-06358-y · 被引用次数：7 · 研究领域：Schizophrenia research and treatment、Machine Learning in Healthcare、Mental Health Treatment and Access

BACKGROUND: Mental disorders are increasingly prevalent, leading to increased medical expenditures. To refine the reimbursement of medical costs for inpatients with mental disorders by health insurance, an accurate prediction model is essential. Per-diem payment is a common internationally implemented payment method for medical insurance of inpatients with mental disorders, necessitating the exploration of advanced machine learning methods for predicting the average daily hospitalization costs (ADHC) based on the characteristics of inpatients with mental disorders. METHODS: We used data including demographic information, clinical/functional characteristics, institutional features, and cost information of 5070 hospitalized patients with mental disorders in Jinhua, China, and employed six algorithms to predict ADHC. Performance of these six algorithms was evaluated through 5- old cross-validation combined with bootstrap method to select the most suitable algorithm and identify key factors influencing ADHC. RESULTS: ) = 0.6417 (95% CI, 0.6236-0.6611), root-mean-square error (RMSE) = 0.2398 (95% CI, 0.2252-0.2553), mean-absolute error (MAE) = 0.1677 (95% CI, 0.1626-0.1735), mean-absolute-percentage error (MAPE) = 0.0295 (95% CI, 0.0287-0.0304)). According to feature importance ranking, models incorporating top 11 factors (> 0.01) demonstrated comparable performance to those encompassing all variables. Top four factors (> 0.05) were level of medical institution, age, functional cl...