使用机器学习澄清精神疾病和重犯之间的关系:一项回顾性研究
Talia R Cohen1,2, Gaylen E Fronk1, Kent A Kiehl3
1Department of Psychology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
PloS one
|February 23, 2024
概括
这项研究发现,在美国监狱样本中,精神疾病和物质使用并不能预测超越犯罪和人口因素的重犯. 然而,治疗精神疾病对于被监禁的人来说仍然至关重要.
科学领域:
- 犯罪学 犯罪学
- 精神病学是一个精神病学.
- 机器学习 机器学习
背景情况:
- 证据表明精神疾病和复发性犯罪之间存在联系是不确定的.
- 监狱人口的精神疾病和物质使用障碍率很高.
- 了解重复犯罪的预测因素对于康复至关重要.
研究的目的:
- 为了研究精神疾病对重复犯罪的独特预测效用.
- 用机器学习分析美国一般监狱人口的重犯预测.
- 为了比较精神疾病,物质使用和人口/犯罪变量的预测能力.
主要方法:
- 对来自美国中西部三个监狱的394名成年囚犯 (322名男性,72名女性) 的回顾性研究.
- 用贝叶斯相关的t测试进行模型比较.
- 使用弹性净物流回归 (GLMnet),k-最近邻居 (KNN) 和随机森林 (RF) 来进行分类.
主要成果:
- 药物使用障碍在86.29%的样本中普遍存在.
- 精神疾病和物质使用变量并没有增强犯罪和人口统计数据之外的重犯预测.
- 只有犯罪和人口统计模型显示,与零模型相比,犯罪复发预测准确度有所提高.
结论:
- 在这个样本中,精神疾病和复发之间没有直接的预测关系.
- 监禁人员的精神疾病治疗是必不可少的,因为高患病率和法律/康复的好处.
- 解决心理健康问题可以改善机构管理和释放后的结果,超越再犯.
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