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Background <p>Mental disorders (MDs) impose substantial long-term inpatient costs, yet existing prediction models rarely account for dynamic hospitalization trajectories or diagnostic heterogeneity. This study developed and validated a dual-track machine learning framework integrating baseline features with trajectory-derived patterns to predict three-year cumulative hospitalization costs for patients with MDs in China.</p> Methods <p>We conducted a retrospective cohort study using electronic health records from 3,396 adults with first admission to a psychiatric hospital (2017–2018) and three‑year follow‑up. State sequence analysis and hierarchical clustering identified distinct hospitalization trajectory patterns. Ten baseline variables available at index admission (Set A) and trajectory cluster membership (Set B) were used to train five regression models with stratified 70:30 split and five‑fold cross‑validation. Performance was evaluated using R², RMSE, and MAE on log‑transformed costs. SHAP (SHapley Additive exPlanations) analysis was applied to interpret the optimal model and examine diagnostic heterogeneity.</p> Results <p>Four distinct trajectory patterns were identified: low‑frequency short‑stay (64.7%), high‑frequency short‑stay (10.0%), long‑term intermittent (4.8%), and long‑term continuous (20.5%). The gradient boosting machine (GBM) achieved the best test performance using Set A (R² = 0.35), significantly outperforming linear regression (R² = 0.33) and random forest (R² = 0.31). Adding trajectory clusters (Set B) increased R² to 0.71 (ΔR² = 0.36), indicating strong association between long‑term hospitalization patterns and cumulative costs, though this component is only retrospectively explanatory. SHAP identified Payment methods, aCCI, Diagnosis groups, and Age as dominant cost drivers. Model performance was stable for the F2 group (61.8% of cohort) but markedly lower for rare diagnostic subgroups (F0, F1).</p> Conclusions <p>Risk stratification for three‑year cumulative hospitalization costs is feasible using only routine baseline information from first admission. The proposed dual‑track framework separates prospective prediction from retrospective explanation, providing a methodologically sound tool for institutional resource planning and high‑risk screening in mental health settings. Future work requires external validation and implementation studies.</p>

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Risk stratification for long-term inpatient costs in mental disorders: a dual-track machine learning approach using baseline EHRs and hospitalization trajectories

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月28日

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  • Mengge Zhang,
  • Guoliang Pan,
  • Haohui Shen,
  • Xiuwen He,
  • Jingyi Xiang,
  • Simeng Wang,
  • Mingyang Yao,
  • Yilong Yang

摘要

Background

Mental disorders (MDs) impose substantial long-term inpatient costs, yet existing prediction models rarely account for dynamic hospitalization trajectories or diagnostic heterogeneity. This study developed and validated a dual-track machine learning framework integrating baseline features with trajectory-derived patterns to predict three-year cumulative hospitalization costs for patients with MDs in China.

Methods

We conducted a retrospective cohort study using electronic health records from 3,396 adults with first admission to a psychiatric hospital (2017–2018) and three‑year follow‑up. State sequence analysis and hierarchical clustering identified distinct hospitalization trajectory patterns. Ten baseline variables available at index admission (Set A) and trajectory cluster membership (Set B) were used to train five regression models with stratified 70:30 split and five‑fold cross‑validation. Performance was evaluated using R², RMSE, and MAE on log‑transformed costs. SHAP (SHapley Additive exPlanations) analysis was applied to interpret the optimal model and examine diagnostic heterogeneity.

Results

Four distinct trajectory patterns were identified: low‑frequency short‑stay (64.7%), high‑frequency short‑stay (10.0%), long‑term intermittent (4.8%), and long‑term continuous (20.5%). The gradient boosting machine (GBM) achieved the best test performance using Set A (R² = 0.35), significantly outperforming linear regression (R² = 0.33) and random forest (R² = 0.31). Adding trajectory clusters (Set B) increased R² to 0.71 (ΔR² = 0.36), indicating strong association between long‑term hospitalization patterns and cumulative costs, though this component is only retrospectively explanatory. SHAP identified Payment methods, aCCI, Diagnosis groups, and Age as dominant cost drivers. Model performance was stable for the F2 group (61.8% of cohort) but markedly lower for rare diagnostic subgroups (F0, F1).

Conclusions

Risk stratification for three‑year cumulative hospitalization costs is feasible using only routine baseline information from first admission. The proposed dual‑track framework separates prospective prediction from retrospective explanation, providing a methodologically sound tool for institutional resource planning and high‑risk screening in mental health settings. Future work requires external validation and implementation studies.