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利用多模式机器学习来准确识别亲密伴侣暴力的风险
Jiayi Gu1,2, Kimberly Villalobos Carballo1,2, Yu Ma1,2
1Trauma Imaging Research and Innovation Center, Brigham and Women's Hospital, Boston, MA USA.
npj women's health
|March 16, 2026
概括
机器学习模型现在可以在患者寻求帮助前几年检测亲密伴侣暴力 (IPV). 这种在临床环境中早期发现滥用行为可以改善患者的安全和健康结果.
科学领域:
- 公共卫生 公共卫生
- 临床信息学 临床信息学
- 机器学习应用 机器学习应用
背景情况:
- 亲密伴侣暴力 (IPV) 是一个重大的公共卫生问题,具有严重的身体和心理健康后果.
- 由于IPV的报道往往不足,因此在临床环境中早期识别对于干预至关重要.
- 现有的IPV检测方法是有限的,突出了需要先进的分析方法.
研究的目的:
- 开发和评估机器学习模型,用于在临床环境中早期检测亲密伴侣暴力 (IPV).
- 通过使用各种临床数据,评估单模和多模模型的性能.
- 为了识别IPV风险的患者,可能在寻求正式帮助之前几年.
主要方法:
- 利用来自家庭虐待干预中心的女性患者数据集.
- 采用表式临床数据和非结构化临床笔记来开发模型.
- 构建单模和多模机器学习模型,包括结合不同数据类型的多模方法.
主要成果:
- 开发的多式联运模型实现了IPV风险识别曲线下的面积 (AUC) 为0.88.
- 该模型证明了能够在寻求帮助的患者之前多年预测IPV风险的能力.
- 对外部数据集的验证,包括不在干预中心寻求帮助的患者和来自不同医院的患者,表现相似.
结论:
- 机器学习,特别是多式联络方法,在医疗保健中早期检测亲密伴侣暴力方面显示出重大前景.
- 这项技术可以实现主动干预,改善IPV幸存者的健康结果.
- 经过验证的模型提供了一个可扩展的解决方案,用于在综合医疗保健网络中识别有风险的个人.
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