ArticleBMC psychiatry2025
Machine-learning-based cost prediction models for inpatients with mental disorders in China.
Article in BMC psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Network Analysis-Driven Machine Learning Model for Identifying High-Cost Stroke Inpatients Using Hospital Discharge Data: Retrospective Study.JMIR medical informatics · 2026Article
- Optimizing case mix for per-diem payment of mental disorders based on E-CHAID decision tree analysis.BMC health services research · 2026Article
- Risk stratification for long-term inpatient costs in mental disorders: a dual-track machine learning approach using baseline EHRs and hospitalization trajectories.BMC health services research · 2026Article
- Artificial intelligence-assisted medical coding and DRG management: current applications, challenges, and future perspectives.Frontiers in medicine · 2026Review
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Authors and funding
5 authors.
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Abstract
backgroundMental 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.
methodsWe 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.
resultsThe random forest (RF) model demonstrated better performance (R-squared (R
conclusionsMachine learning algorithms, particularly RF algorithm, enhance accuracy of predicting ADHC for mental health patients. The findings of this study provide evidence for setting up more reasonable insurance payment standards for inpatients with mental disorders and support resource allocation in clinical practice.
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