Citation

Sachdeva AS, Bell A, Furst DJ, Kozlowski DA, Crabtree-Nelson S, Raicu D. AMIA Jt. Summits Transl. Sci. Proc. 2024; 2024: 401-408.

Copyright

(Copyright © 2024, American Medical Informatics Association)

DOI

unavailable

PMID

38827106

PMCID

PMC11141795

Abstract

Research studies have presented an unappreciated relationship between intimate partner violence (IPV) survivors and symptoms of traumatic brain injuries (TBI). Within these IPV survivors, resulting TBIs are not always identified during emergency room visits. This demonstrates a need for a prescreening tool that identifies IPV survivors who should receive TBI screening. We present a model that measures similarities to clinical reports for confirmed TBI cases to identify whether a patient should be screened for TBI. This is done through an ensemble of three supervised learning classifiers which work in two distinct feature spaces. Individual classifiers are trained on clinical reports and then used to create an ensemble that needs only one positive label to indicate a patient should be screened for TBI.

Language: en