Frontiers in cellular and infection microbiology · 2026
Development of a machine learning-based mortality prediction model for patients with mental disorders and COVID-19.
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Zhang Yi, Wang Haiyu, Yang Mengzhao, Cui Zhenti, Wang Juan, Qian Guowu, Li Silin, Luo Hong, Zhang Shixi, Wang Ling, Zhang Donghua, Li Guotao, Hu Xinjun, Bai Qin, Ren Zhigang
Аннотация
INTRODUCTION: Patients with mental disorders are at increased risk of adverse outcomes from COVID-19, but prognostic evidence specific to this population remains limited. This study aimed to develop and validate machine-learning models for predicting 31-day mortality among hospitalized patients with mental disorders and laboratory-confirmed COVID-19.
METHODS: Data were retrospectively collected from 439 hospitalized patients across 10 hospitals in Henan Province, China. Patients were randomly divided into a training cohort (n = 308) and an independent test cohort (n = 131). Oversampling was applied during model development to address class imbalance. Candidate predictors were selected using LASSO, Boruta, and random forest methods, and eight machine-learning algorithms were trained. SHAP analysis was used for model interpretation, and Kaplan-Meier analysis compared survival between model-defined risk groups.
RESULTS: Patients were generally older, 63.1% were female, and comorbidities were common. Several models showed good discrimination in the training cohort, although some showed overfitting. In the test cohort, the neural network model with LASSO-selected features performed best, with an AUC of 0.911 (95% CI: 0.832-0.990). SHAP analysis identified concomitant hormone therapy, alkaline phosphatase, and lymphocyte count as the leading predictors. The high-risk group had significantly higher cumulative mortality than the low-risk group (log-rank P < 0.0001).
DISCUSSION: A machine-learning model based on routine clinical and laboratory variables may support short-term mortality risk stratification in this regional multicenter cohort.
METHODS: Data were retrospectively collected from 439 hospitalized patients across 10 hospitals in Henan Province, China. Patients were randomly divided into a training cohort (n = 308) and an independent test cohort (n = 131). Oversampling was applied during model development to address class imbalance. Candidate predictors were selected using LASSO, Boruta, and random forest methods, and eight machine-learning algorithms were trained. SHAP analysis was used for model interpretation, and Kaplan-Meier analysis compared survival between model-defined risk groups.
RESULTS: Patients were generally older, 63.1% were female, and comorbidities were common. Several models showed good discrimination in the training cohort, although some showed overfitting. In the test cohort, the neural network model with LASSO-selected features performed best, with an AUC of 0.911 (95% CI: 0.832-0.990). SHAP analysis identified concomitant hormone therapy, alkaline phosphatase, and lymphocyte count as the leading predictors. The high-risk group had significantly higher cumulative mortality than the low-risk group (log-rank P < 0.0001).
DISCUSSION: A machine-learning model based on routine clinical and laboratory variables may support short-term mortality risk stratification in this regional multicenter cohort.