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Interpretable machine learning model to predict aggressive behavior in first-episode schizophrenia.

Hua Fan1, Zexi Yang2, Xinyi Yu2

  • 1Beijing An Ding Hospital, Beijing, PR China.

Journal of Psychiatric Research
|October 4, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models can predict aggressive behavior in first-episode schizophrenia patients. The Random Survival Forest model showed high accuracy, identifying impulsivity and income as key risk factors.

Keywords:
AggressionCox modelMachine learningROC curveSchizophrenia

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Area of Science:

  • Psychiatry
  • Machine Learning
  • Behavioral Science

Background:

  • Schizophrenia is a complex mental disorder.
  • Aggressive behavior is a significant concern in first-episode schizophrenia patients.
  • Predicting aggressive behavior is crucial for timely intervention.

Purpose of the Study:

  • To predict the risk of aggressive behavior in first-episode schizophrenia patients.
  • To evaluate the performance of time-to-event machine learning models for this prediction.
  • To identify key predictors of aggressive behavior in this population.

Main Methods:

  • A cohort study design was employed with 216 first-episode schizophrenia patients.
  • Patients were followed for 24 months to assess aggressive behavior.
  • Three survival models were developed and evaluated using metrics like C-index, time-dependent AUC, and AUPRC. Shapley Additive Explanation (SHAP) was used for feature importance.

Main Results:

  • Aggressive behavior was observed in 16.67% of patients during follow-up.
  • The Random Survival Forest model exhibited the best performance (C-index=0.79, time-dependent AUC=0.91, AUPRC=0.59).
  • High impulsivity, higher average monthly income, larger household size, unemployment, and lower EPQ-L scores were identified as significant predictors.

Conclusions:

  • The Random Survival Forest model is effective in predicting aggressive behavior in first-episode schizophrenia.
  • Impulsivity and average monthly income are the most significant factors influencing aggressive behavior.
  • These findings can aid in developing targeted interventions for at-risk individuals.