可解释的机器学习,以识别在亲密伴侣暴力中复发的风险因素
Çeragğ Ogğuztüzün1, Mehmet Koyutürk2, Günnur Karakurt1
1Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106, USA.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
概括
预测亲密伴侣暴力 (IPV) 再犯率至关重要. 机器学习模型发现,减少物质使用和与伴侣接触等因素降低了IPV违法者重犯风险.
科学领域:
- 公共卫生 公共卫生
- 犯罪学 犯罪学
- 机器学习 机器学习
背景情况:
- 亲密伴侣暴力 (IPV) 是一个主要的全球健康问题,其发生率越来越高.
- 预测IPV罪犯的复发是有效的预防和干预策略的关键.
- 现有的风险评估工具可能缺乏标准化,并与非线性关系作斗争.
研究的目的:
- 开发可解释的机器学习模型,用于预测IPV犯罪者之间的身体攻击复发率.
- 确定与重犯风险相关的关键特征.
- 提高IPV风险评估的客观性和准确性.
主要方法:
- 使用了四年临床研究数据集.
- 应用过的目标编码来标准化严重性得分和处理非线性关联.
- 开发可解释的机器学习模型来分析特征的重要性.
主要成果:
- 结合自我报告和合作伙伴报告的变量,显著提高了预测准确性.
- 减少物质使用和避免伴侣接触与较低的复发风险有关.
- 分离过程被确定为与更高的重犯可能性相关的因素.
结论:
- 开发的机器学习模型为IPV复发风险因素提供了更细致的理解.
- 结果可以为开发更有效的IPV治疗和管理策略提供信息.
- 改进的风险评估可以帮助解决IPV护理中的差异,并降低重犯率.
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