A natural language processing and deep learning approach to identify child abuse from pediatric electronic medical records.

ABSTRACT: Child physical abuse is a leading cause of traumatic injury and death in children. In 2017, child abuse was responsible for 1688 fatalities in the United States, of 3.5 million children referred to Child Protection Services and 674,000 substantiated victims. While large referral hospitals maintain teams trained in Child Abuse Pediatrics, smaller community hospitals often do not have such dedicated resources to evaluate patients for potential abuse. Moreover, identification of abuse has a low margin of error, as false positive identifications lead to unwarranted separations, while false negatives allow dangerous situations to continue. This context makes the consistent detection of and response to abuse difficult, particularly given subtle signs in young, non-verbal patients. Here, we descr

SUBMITTER: Annapragada AV 

PROVIDER: S-EPMC7909689 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature