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 full dataset includes 34,642 radiology reports and 1479 patients of IPV victims and control patients. We are able to accurately predict IPV victims and injury labels, and our best model predicts IPV a median of 1.34 years before violence prevention program entry with a sensitivity of  95 %DIFDELCMD < \\%%% %DIF < and a specificity of 71\\%. Our findings align with known clinical patterns of IPV injuries. 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.abstract 
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 %DIFDELCMD < \\%%% %DIF < and a specificity of 71\\%. Our findings align with known clinical patterns of IPV injuries. 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.abstract \%. 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.
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We are able to accurately predict IPV victims and injury labels, and our
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1.34
3.08
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64\% and a specificity of
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\%. 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.