Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA* Corresponding author.,
Pac Symp Biocomput. 2021;26:55-66.
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.
亲密伴侣暴力 (IPV) 是一个紧迫、普遍但未被充分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是否存在 IPV 和伤害。我们根据以下两种情况，利用放射学报告来训练预测算法：1）基于进入暴力预防项目的 IPV 标签；2）由接受过急诊放射学奖学金培训的医生提供的伤害标签。我们的数据集包括 34642 份放射学报告和 1479 名 IPV 受害者和对照患者。我们最好的模型在进入暴力预防项目前 3.08 年就能预测到 IPV，其敏感性为 64%，特异性为 95%。我们进行了错误分析，以确定模型对哪些患者的性能特别高或特别低，并讨论了部署临床风险模型的下一步措施。
