Intimate Partner Violence and Injury Prediction From Radiology Reports
Name
intimate-partner-violence-and-injury-prediction-from-radiology-reports.pdf
Description
Published version
Size
444.83 KB
Format
Adobe PDF
Checksum (MD5)
200b7bffcc7eed8acf64fc5508f2f203
Author(s)
Chen, Irene Y
Alsentzer, Emily
Park, Hyesun
Thomas, Richard
Gosangi, Babina
Gujrathi, Rahul
Khurana, Bharti
Date Issued
November 2020
Journal
Biocomputing 2021
Publisher
WORLD SCIENTIFIC
Version
Final published version
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%. 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.
Terms of Use
Creative Commons Attribution-NonCommercial
Persistent DSpace Link
DOI of Published Version
10.1142/9789811232701_0006
