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For: Gou N, Xiang Y, Zhou J, Zhang S, Zhong S, Lu J, Liang X, Liu J, Wang X. Identification of violent patients with schizophrenia using a hybrid machine learning approach at the individual level. Psychiatry Res 2021;306:114294. [PMID: 34823086 DOI: 10.1016/j.psychres.2021.114294] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
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1 Parmigiani G, Barchielli B, Casale S, Mancini T, Ferracuti S. The impact of machine learning in predicting risk of violence: A systematic review. Front Psychiatry 2022;13. [DOI: 10.3389/fpsyt.2022.1015914] [Reference Citation Analysis]
2 Yan C, Ding C, Duan G. PMMS: Predicting essential miRNAs based on multi-head self-attention mechanism and sequences. Front Med 2022;9. [DOI: 10.3389/fmed.2022.1015278] [Reference Citation Analysis]
3 Yu T, Pei W, Xu C, Zhang X, Deng C. Prediction of violence in male schizophrenia using sMRI, based on machine learning algorithms. BMC Psychiatry 2022;22:676. [PMID: 36320010 DOI: 10.1186/s12888-022-04331-1] [Reference Citation Analysis]
4 Sonnweber M, Lau S, Kirchebner J. Exploring Characteristics of Homicide Offenders With Schizophrenia Spectrum Disorders Via Machine Learning. Int J Offender Ther Comp Criminol 2022;:306624X221102799. [PMID: 35730542 DOI: 10.1177/0306624X221102799] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Tesli N, Bell C, Hjell G, Fischer-vieler T, I Maximov I, Richard G, Tesli M, Melle I, Andreassen OA, Agartz I, Westlye LT, Friestad C, Haukvik UK, Rokicki J. The age of violence: Mapping brain age in psychosis and psychopathy. NeuroImage: Clinical 2022;36:103181. [DOI: 10.1016/j.nicl.2022.103181] [Reference Citation Analysis]