Intimate partner violence (IPV) is an urgent, prevalent, and under-detected public health issue. We present machine learning models to assess patients for IPV and injury. We train the predictive algorithms on radiology reports with 1) IPV labels based on entry to a violence prevention program and 2) injury labels provided by emergency radiology fellowship-trained physicians. Our dataset includes 34,642 radiology reports and 1479 patients of IPV victims and control patients. Our best model predicts IPV a median of 3.08 years before violence prevention program entry with a sensitivity of 64% and a specificity of 95%. We conduct error analysis to determine for which patients our model has especially high or low performance and discuss next steps for a deployed clinical risk model.

中文翻译：

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来自放射学报告的亲密伴侣暴力和伤害预测

亲密伴侣暴力 (IPV) 是一个紧迫、普遍且未被发现的公共卫生问题。我们提出了机器学习模型来评估患者的 IPV 和损伤。我们对放射学报告的预测算法进行训练，其中包括 1) 基于加入暴力预防计划的 IPV 标签和 2) 由接受过急诊放射学研究金培训的医生提供的伤害标签。我们的数据集包括 34,642 份放射学报告和 1479 名 IPV 受害者和对照患者。我们的最佳模型在进入暴力预防计划之前预测 IPV 的中位数为 3.08 年，敏感性为 64%，特异性为 95%。我们进行错误分析以确定我们的模型对哪些患者具有特别高或特别低的性能，并讨论部署的临床风险模型的后续步骤。