Department of Forensic Psychiatry, Psychiatric Hospital, University of Zurich, Zurich, Switzerland.
Department of Forensic Psychiatry, Psychiatric Hospital, University of Zurich, Zurich, Switzerland.
Compr Psychiatry. 2021 May;107:152238. doi: 10.1016/j.comppsych.2021.152238. Epub 2021 Mar 9.
The link between schizophrenia and violent offending has long been the subject of research with significant impact on mental health policy, clinical practice and public perception of the dangerousness of people with psychiatric disorders. The present study attempts to identify factors that differentiate between violent and non-violent offenders based on a unique sample of 370 forensic offender patients with schizophrenia spectrum disorder by employing machine learning algorithms and an extensive set of variables.
Using machine learning algorithms, 519 variables were explored in order to differentiate violent and non-violent offenders. To minimize the risk of overfitting, the dataset was split, employing variable filtering, machine learning model building and selection embedded in a nested resampling approach on one subset. The best model was then selected, and the most important variables applied on the second data subset.
Ten factors regarding criminal and psychiatric history as well as clinical, developmental, and social factors were identified to be most influential in differentiating between violent and non-violent offenders and are discussed in light of prior research on this topic. With an AUC of 0.76, a sensitivity of 72% and a specificity of 62%, a correct classification into violent and non-violent offences could be determined in almost three quarters of cases.
Our findings expand current research on the factors influencing violent offending in patients with SSD, which is crucial for the development of preventive and therapeutic strategies that could potentially reduce the prevalence of violence in this population. Limitations, clinical relevance and future directions are discussed.
精神分裂症与暴力犯罪之间的关联一直是研究的主题，对心理健康政策、临床实践和公众对精神障碍患者危险性的看法产生了重大影响。本研究试图通过使用机器学习算法和广泛的变量，从一个独特的 370 名精神分裂症谱系障碍法医罪犯患者样本中，确定区分暴力和非暴力罪犯的因素。
使用机器学习算法，探索了 519 个变量，以区分暴力和非暴力罪犯。为了最小化过拟合的风险，数据集被分割，在嵌套重采样方法的一个子集上采用变量过滤、机器学习模型构建和选择。然后选择最佳模型，并将最重要的变量应用于第二个数据集子集。
确定了 10 个因素，涉及犯罪和精神病史以及临床、发育和社会因素，这些因素在区分暴力和非暴力罪犯方面最具影响力，并根据该主题的先前研究进行了讨论。AUC 为 0.76，灵敏度为 72%，特异性为 62%，在近四分之三的情况下可以正确地将暴力和非暴力犯罪分类。
我们的研究结果扩展了当前关于影响 SSD 患者暴力犯罪的因素的研究，这对于制定预防和治疗策略至关重要，这些策略可能会降低该人群暴力行为的发生率。讨论了局限性、临床相关性和未来方向。
