论文标题
放射学报告中的亲密伴侣暴力和伤害预测
Intimate Partner Violence and Injury Prediction From Radiology Reports
论文作者
论文摘要
亲密的伴侣暴力（IPV）是一个紧急，普遍且未发现的公共卫生问题。我们提出机器学习模型，以评估患者IPV和受伤。我们在放射学报告上使用1）IPV标签培训了基于进入暴力预防计划的IPV标签的预测算法，以及2）紧急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34,642个放射学报告和1479名IPV受害者和对照患者的患者。我们的最佳模型预测IPV在预防暴力计划进入之前的中位数为3.08年，灵敏度为64％，特异性为95％。我们进行错误分析，以确定我们的模型的患者的性能特别高或低性能，并讨论部署的临床风险模型的下一步。
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.
