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ORIGINAL RESEARCH article

Front. Psychiatry

Sec. Schizophrenia

Volume 16 - 2025 | doi: 10.3389/fpsyt.2025.1644341

This article is part of the Research TopicMachine Learning Algorithms and Software Tools for Early Detection and Prognosis of SchizophreniaView all 8 articles

Hierarchical machine learning model integrating clinical history and nursing observations for predicting violent behavior in hospitalized schizophrenia patients

Provisionally accepted
Xianfeng  MengXianfeng Meng1Liang  WangLiang Wang1Ying  DuanYing Duan2*Gang  ZHUGang ZHU3Jinhuan  WangJinhuan Wang1Ying  SunYing Sun1Mingtao  WangMingtao Wang1Miao  LiuMiao Liu1Chenhui  SunChenhui Sun1Longlong  PangLonglong Pang4,5,6Kunyuan  HuKunyuan Hu4,5,6Wei  YangWei Yang4,5,6*Wei  ShaoWei Shao7Jintao  RenJintao Ren1xiaojun  shaoxiaojun shao3Yang  ZhangYang Zhang1
  • 1Liaoning Provincial Mental Health Center, Tieling, China
  • 2Liaoning Maternal and Child Health Hospital, Shenyang, China
  • 3Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
  • 4Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, shenyang, China
  • 5Chinese Academy of Sciences Shenyang Institute of Automation, Shenyang, China
  • 6University of the Chinese Academy of Sciences, Beijing, China
  • 7Shengjing Hospital of China Medical University, Shenyang, China

The final, formatted version of the article will be published soon.

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 (n = 123) and non-violent (n = 223) groups based on documented aggressive incidents. Eighteen static clinical variables (e.g., age, gender, history of violence, manic symptoms) were extracted from electronic medical records, and 39 dynamic behavioral indicators (e.g., anger expression, insomnia, auditory hallucinations) were assessed weekly using the Psychiatric Patient Nursing Observation Scale. Predictive models were separately developed using six machine learning algorithms: Regularized Logistic Regression (LR), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Multi-layer Perceptron (MLP), and K-Nearest Neighbor (KNN). Regularized logistic regression was selected as the final algorithm due to its superior predictive performance, indicated by the highest Area Under the Curve (AUC), in both static baseline and dynamic behavioral models. A hierarchical predictive model was then established using regularized logistic regression separately for static baseline risk and dynamic risk fluctuations, subsequently integrated using a weighted fusion approach. 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 (α) of 0.37, balancing sensitivity (0.7838), specificity (0.8358), and accuracy (0.8173). Key independent predictors included static factors such as history of violence (odds ratio [OR]=4.638), manic symptoms (OR=7.801), younger age (OR=0.966), high-risk command hallucinations (OR=2.602), and dynamic features like anger expression (OR=4.649), insomnia (OR=7.422), and auditory hallucinations (OR=2.092). 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.

Keywords: Schizophrenia, Violent Behavior, predictive models, machine learning, Hierarchical model, Risk factors

Received: 10 Jun 2025; Accepted: 02 Sep 2025.

Copyright: © 2025 Meng, Wang, Duan, ZHU, Wang, Sun, Wang, Liu, Sun, Pang, Hu, Yang, Shao, Ren, shao and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Ying Duan, Liaoning Maternal and Child Health Hospital, Shenyang, China
Wei Yang, Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, shenyang, China

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