ArticleFrontiers in psychiatry2025
Hierarchical machine learning model integrating clinical history and nursing observations for predicting violent behavior in hospitalized schizophrenia patients.
Article in Frontiers in psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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16 authors.
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Abstract
Objective: To develop and validate a hierarchical machine learning model integrating static clinical features and dynamic behavioral assessments for accurately predicting violent behaviors among hospitalized schizophrenia patients. Methods: This retrospective study included 346 schizophrenia patients hospitalized from July 2021 to July 2024 in Liaoning Province. Patients were categorized into violent ( Results: The integrated hierarchical regularized logistic regression model achieved an optimal performance with an area under the curve (AUC) of 0.8741, surpassing both the static baseline model (AUC = 0.7953) and dynamic model (AUC = 0.8003) alone. Optimal predictive performance was obtained with a fusion parameter ( Conclusion: The hierarchical machine learning model integrating clinical history and dynamic nursing observations significantly enhances predictive accuracy for violent behavior in schizophrenia inpatients, providing clinicians with valuable tools for timely risk assessment and personalized preventive interventions.
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