BACKGROUND: Identifying non-accidental trauma (NAT) in pediatric trauma patients is challenging. We developed a machine learning model that uses demographic characteristics and ICD10 codes to detect the first diagnosis of NAT.
BACKGROUND: Research on child protective services (CPS) is impeded by a lack of high-quality structured data. Crucial information on cases is often documented in case files, but only in narrative form. Researchers have applied automated language proc...
BACKGROUND: Rates of child maltreatment (CM) obtained from electronic health records are much lower than national child welfare prevalence rates indicate. There is a need to understand how CM is documented to improve reporting and surveillance.
Childhood sexual abuse (CSA) is a worldwide phenomenon that has negative long-term consequences for the victims and their families, and inflicts a considerable economic toll on society. One of the main difficulties in treating CSA is victims' relucta...
BACKGROUND: State child welfare agencies collect, store, and manage vast amounts of data. However, they often do not have the right data, or the data is problematic or difficult to inform strategies to improve services and system processes. Considera...