PDF(Pubmed)
Abstract：
UNASSIGNED: Comorbid substance use disorder (SUD) is linked to a higher risk of violence in patients with schizophrenia spectrum disorder (SSD). The objective of this study is to explore the most distinguishing factors between offending and non-offending patients diagnosed with SSD and comorbid SUD using supervised machine learning.
UNASSIGNED: A total of 269 offender patients and 184 non-offender patients, all diagnosed with SSD and SUD, were assessed using supervised machine learning algorithms.
UNASSIGNED: Failures during opening, referring to rule violations during a permitted temporary leave from an inpatient ward or during the opening of an otherwise closed ward, was found to be the most influential distinguishing factor, closely followed by non-compliance with medication (in the psychiatric history). Following in succession were social isolation in the past, no antipsychotics prescribed (in the psychiatric history), and no outpatient psychiatric treatments before the current hospitalization.
UNASSIGNED: This research identifies critical factors distinguishing offending patients from non-offending patients with SSD and SUD. Among various risk factors considered in prior research, this study emphasizes treatment-related differences between the groups, indicating the potential for improvement regarding access and maintenance of treatment in this particular population. Further research is warranted to explore the relationship between social isolation and delinquency in this patient population.
UNASSIGNED: A total of 269 offender patients and 184 non-offender patients, all diagnosed with SSD and SUD, were assessed using supervised machine learning algorithms.
UNASSIGNED: Failures during opening, referring to rule violations during a permitted temporary leave from an inpatient ward or during the opening of an otherwise closed ward, was found to be the most influential distinguishing factor, closely followed by non-compliance with medication (in the psychiatric history). Following in succession were social isolation in the past, no antipsychotics prescribed (in the psychiatric history), and no outpatient psychiatric treatments before the current hospitalization.
UNASSIGNED: This research identifies critical factors distinguishing offending patients from non-offending patients with SSD and SUD. Among various risk factors considered in prior research, this study emphasizes treatment-related differences between the groups, indicating the potential for improvement regarding access and maintenance of treatment in this particular population. Further research is warranted to explore the relationship between social isolation and delinquency in this patient population.
摘要：
■合并药物使用障碍（SUD）与精神分裂症谱系障碍（SSD）患者的较高暴力风险有关。这项研究的目的是探索使用监督机器学习诊断为SSD和并发SUD的违规和非违规患者之间最具区别的因素。
■共有269名罪犯患者和184名非罪犯患者，所有诊断为SSD和SUD，使用监督机器学习算法进行评估。
■打开时出现故障,指在住院病房允许的临时休假期间或在其他封闭病房开放期间违反规则，被发现是最具影响力的区别因素，紧随其后的是不遵守药物（精神病史）。随之而来的是过去的社会孤立，没有处方抗精神病药（在精神病史），目前住院前没有门诊精神病治疗。
■这项研究确定了区分SSD和SUD患者的关键因素。在先前研究中考虑的各种风险因素中，这项研究强调了组间治疗相关的差异，表明在这一特定人群中治疗的获取和维持方面的潜力。需要进一步的研究来探索该患者人群中社会隔离与犯罪之间的关系。
■共有269名罪犯患者和184名非罪犯患者，所有诊断为SSD和SUD，使用监督机器学习算法进行评估。
■打开时出现故障,指在住院病房允许的临时休假期间或在其他封闭病房开放期间违反规则，被发现是最具影响力的区别因素，紧随其后的是不遵守药物（精神病史）。随之而来的是过去的社会孤立，没有处方抗精神病药（在精神病史），目前住院前没有门诊精神病治疗。
■这项研究确定了区分SSD和SUD患者的关键因素。在先前研究中考虑的各种风险因素中，这项研究强调了组间治疗相关的差异，表明在这一特定人群中治疗的获取和维持方面的潜力。需要进一步的研究来探索该患者人群中社会隔离与犯罪之间的关系。
