机器学习和精神病患者自杀的预测:一个系统的审查
Alessandro Pigoni1,2, Giuseppe Delvecchio2, Nunzio Turtulici3
1Social and Affective Neuroscience Group, MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy.
Translational psychiatry
|March 9, 2024
概括
机器学习 (ML) 对精神病患者的自杀预测有希望. 虽然临床因素是关键因素,但ML模型需要更多的数据来提高准确性和临床使用.
科学领域:
- 精神病学是一个精神病学.
- 计算神经科学是一种神经科学.
- 数据科学数据科学数据科学
背景情况:
- 机器学习 (ML) 提供了改善自杀风险预测的潜力.
- 以前的研究经常混合精神病患者和非精神病患者群体,限制了对特定风险因素的关注.
- 正式的精神病诊断可能会掩盖更微妙的,特定于人口的风险指标.
研究的目的:
- 系统地审查专注于精神病临床群体内自杀行为的ML研究.
- 确定有效的ML算法和关键预测器,在这个特定的人群中预测自杀风险.
- 突出精神病学自杀预测中的ML的局限性和未来方向.
主要方法:
- 按照PRISMA指南在PubMed,EMBASE和Scopus (开始至2022年11月17日) 进行系统的文献搜索.
- 包括使用ML进行精神病患者自杀风险评估或预测的原始研究.
- 使用个人预后或诊断多变量预测模型透明报告 (TRIPOD) 准则进行偏差风险评估.
主要成果:
- 从1032项检索中,包括了81项研究,重点是精神病患者群体.
- 临床和人口特征是最常用的预测因素.
- 与其他算法相比,随机森林,支向量机器和卷积神经网络显示出更高的准确性.
- 大多数研究报告的准确率为70%或更高,利用诸如先前尝试和疾病严重程度等特征.
结论:
- 在精神病患者群体中,ML显示出对自杀性预测的前景,临床和人口统计数据是重要的预测因素.
- 目前的ML模型面临局限性,包括缺乏神经生物学/成像数据和外部验证.
- 进一步的研究和克服当前的局限性对于开发临床上适用的ML工具来减少自杀死亡率至关重要.
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