机器学习用于预测精神分裂症谱系障碍中的暴力行为:系统性审查
Mohammadamin Parsaei1, Alireza Arvin2, Morvarid Taebi2
1Maternal, Fetal & Neonatal Research Center, Family Health Research Institute, Tehran University of Medical Sciences, Tehran, Iran.
机器学习模型在预测精神分裂症谱系障碍 (SSD) 中的暴力行为 (VB) 方面表现有希望. 梯度增强模型表现最好,确定年龄和药物剂量是改善患者安全的关键预测因素.
科学领域:
- 精神病学和行为科学
- 计算神经科学是一种神经科学.
- 医疗信息学 医疗信息学
背景情况:
- 精神分裂症谱系障碍 (SSD) 与暴力行为 (VB) 的风险增加有关.
- 在SSD患者中预测VB可以减轻对个体的伤害,并减少医疗负担.
- 机器学习 (ML) 提供了识别有风险的SSD患者的潜力.
研究的目的:
- 系统地审查应用ML来预测SSD患者VB的研究.
- 在这个人群中确定最有效的ML方法来预测VB.
- 为了确定SSD中VB的关键预测因素.
主要方法:
- 在PubMed,科学网,Embase和PsycINFO进行了系统的文献搜索.
- 包括到2023年9月30日发表的研究,重点是SSD中VB预测的ML.
- 分析了18项涉及11733名SSD患者的研究数据.
主要成果:
- ML模型在预测VB时表现出可变的性能 (AUC为0.56-0.95,准确度为50.27-90.67%) .
- 与其他ML方法相比,梯度增强模型表现出优异的性能.
- 年龄和离院时的 olanzapine 相当剂量是最常见的 VB 预测因素.
结论:
- 机器学习模型,特别是梯度增强,显示了预测SSD患者VB的潜力.
- 需要进一步的研究才能将这些ML发现转化为临床实践.
- 识别关键预测因素可以帮助针对性干预和风险管理.
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