被诊断患有精神分裂症谱系障碍的犯罪者和非犯罪者的社会人口统计变量 - - 使用机器学习的探索性分析
Andreas B Hofmann1, Marc Dörner2,3, Lena Machetanz1,4
1Adult Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, Faculty of Medicine, University of Zurich, 8006 Zurich, Switzerland.
Healthcare (Basel, Switzerland)
|September 14, 2024
概括
机器学习 (ML) 分析了精神分裂症患者的社会人口统计因素. 该研究发现,使用这些变量,犯罪者和非犯罪者群体之间的歧视较差.
科学领域:
- 法医精神病学 法医精神病学
- 心理健康研究 心理健康研究
- 计算统计学 计算统计学
背景情况:
- 机器学习 (ML) 为复杂的数据分析提供先进的统计方法.
- ML在模式检测,相关性识别和事件预测方面表现出色.
- 这些能力对于诸如精神分裂症谱系障碍等多因素性疾病至关重要.
研究的目的:
- 调查精神分裂症谱系障碍的犯罪者和非犯罪者患者之间的社会人口统计差异.
- 使用ML算法构建和评估一个歧视模型.
- 评估社会人口统计学变量在区分患者群体中的预测能力.
主要方法:
- 分析了740名患有精神分裂症谱系障碍的患者 (370名犯罪者,370名非犯罪者) 的样本.
- 使用7个ML算法测试了48个社会人口统计变量.
- 渐变增强被确定为模型构建中最适合的算法.
主要成果:
- 最终的歧视性模式包括出生国,居住状态和教育状态.
- 该模型实现了曲线下的面积 (AUC) 为0.65.
- 这表明犯罪者和非犯罪者患者之间的统计歧视不佳,仅基于所选的社会人口统计学变量.
结论:
- 只有社会人口统计学因素在区分精神分裂症谱系障碍患者的犯罪者和非犯罪者的有用性有限.
- 进一步的研究应该探索额外的临床或生物变量,以改善歧视.
- 机器学习为量化精神病学研究中的模型性能提供了一个框架.
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