  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.
