Application of machine learning in predicting aggressive behaviors from hospitalized patients with schizophrenia

作者：Nuo Cheng, Meihao Guo, Fang Yan, Zhengjun Guo, Jun Meng, Kui Ning, Yanping Zhang, Zitian Duan, Yong Han, Changhong Wang · 发表于：Frontiers in Psychiatry · 年份：2023 · DOI：10.3389/fpsyt.2023.1016586 · 被引用次数：9 · 研究领域：Psychopathy, Forensic Psychiatry, Sexual Offending、Schizophrenia research and treatment、Tryptophan and brain disorders

Objective To establish a predictive model of aggressive behaviors from hospitalized patients with schizophrenia through applying multiple machine learning algorithms, to provide a reference for accurately predicting and preventing of the occurrence of aggressive behaviors. Methods The cluster sampling method was used to select patients with schizophrenia who were hospitalized in our hospital from July 2019 to August 2021 as the survey objects, and they were divided into an aggressive behavior group (611 cases) and a non-aggressive behavior group (1,426 cases) according to whether they experienced obvious aggressive behaviors during hospitalization. Self-administered General Condition Questionnaire, Insight and Treatment Attitude Questionnaire (ITAQ), Family APGAR (Adaptation, Partnership, Growth, Affection, Resolve) Questionnaire (APGAR), Social Support Rating Scale Questionnaire (SSRS) and Family Burden Scale of Disease Questionnaire (FBS) were used for the survey. The Multi-layer Perceptron, Lasso, Support Vector Machine and Random Forest algorithms were used to build a predictive model for the occurrence of aggressive behaviors from hospitalized patients with schizophrenia and to evaluate its predictive effect. Nomogram was used to build a clinical application tool. Results The area under the receiver operating characteristic curve (AUC) values of the Multi-Layer Perceptron, Lasso, Support Vector Machine, and Random Forest were 0.904 (95% CI: 0.877–0.926), 0.901 (95% CI: 0...