Parsaei Mohammadamin, Arvin Alireza, Taebi Morvarid, Seyedmirzaei Homa, Cattarinussi Giulia, Sambataro Fabio, Pigoni Alessandro, Brambilla Paolo, Delvecchio Giuseppe
Maternal, Fetal & Neonatal Research Center, Family Health Research Institute, Tehran University of Medical Sciences, Tehran, Iran.
Center for Orthopedic Trans-disciplinary Applied Research (COTAR), Tehran University of Medical Sciences, Tehran, Iran.
Front Psychiatry. 2024 Mar 21;15:1384828. doi: 10.3389/fpsyt.2024.1384828. eCollection 2024.
Schizophrenia spectrum disorders (SSD) can be associated with an increased risk of violent behavior (VB), which can harm patients, others, and properties. Prediction of VB could help reduce the SSD burden on patients and healthcare systems. Some recent studies have used machine learning (ML) algorithms to identify SSD patients at risk of VB. In this article, we aimed to review studies that used ML to predict VB in SSD patients and discuss the most successful ML methods and predictors of VB.
We performed a systematic search in PubMed, Web of Sciences, Embase, and PsycINFO on September 30, 2023, to identify studies on the application of ML in predicting VB in SSD patients.
We included 18 studies with data from 11,733 patients diagnosed with SSD. Different ML models demonstrated mixed performance with an area under the receiver operating characteristic curve of 0.56-0.95 and an accuracy of 50.27-90.67% in predicting violence among SSD patients. Our comparative analysis demonstrated a superior performance for the gradient boosting model, compared to other ML models in predicting VB among SSD patients. Various sociodemographic, clinical, metabolic, and neuroimaging features were associated with VB, with age and olanzapine equivalent dose at the time of discharge being the most frequently identified factors.
ML models demonstrated varied VB prediction performance in SSD patients, with gradient boosting outperforming. Further research is warranted for clinical applications of ML methods in this field.
精神分裂症谱系障碍（SSD）可能与暴力行为（VB）风险增加有关，暴力行为会对患者、他人及财产造成伤害。预测暴力行为有助于减轻SSD对患者和医疗系统的负担。最近一些研究使用机器学习（ML）算法来识别有暴力行为风险的SSD患者。在本文中，我们旨在回顾使用ML预测SSD患者暴力行为的研究，并讨论最成功的ML方法和暴力行为预测因素。
2023年9月30日，我们在PubMed、科学网、Embase和PsycINFO中进行了系统检索，以识别关于ML在预测SSD患者暴力行为中的应用的研究。
我们纳入了18项研究，数据来自11733例被诊断为SSD的患者。不同的ML模型表现各异，在预测SSD患者暴力行为时，受试者工作特征曲线下面积为0.56 - 0.95，准确率为50.27% - 90.67%。我们的比较分析表明，与其他ML模型相比，梯度提升模型在预测SSD患者暴力行为方面表现更优。各种社会人口统计学、临床、代谢和神经影像学特征与暴力行为相关，年龄和出院时奥氮平等效剂量是最常确定的因素。
ML模型在SSD患者暴力行为预测中表现出不同的性能，梯度提升模型表现更优。该领域ML方法的临床应用值得进一步研究。
