A neural network model analysis to identify victims of intimate partner violence.
The objective of this study was to determine if a neural network model can identify victims of intimate partner violence (IPV). A custom neural network model was constructed and trained using the 1995 ED databases at Truman Medical Center of all female visits. The input vector developed was an array of 100 binary elements containing, in coded form, the patient's age, day of week, primary diagnosis (excluding 995.81), disposition, race, time, and E-code. The trained network was then presented with a series of 19,830 female patients from the 1996 ED database to determine if it could discriminate cases from control subjects. The neural network identified 231 of 297 known IPV victims (sensitivity 78%) in the 1996 database. It also categorized 2234 false-positive patients out of 19,533 IPV-negative patients (specificity 89%). A computer-based neural network model, when supplied with information commonly available in the ED medical record, can identify victims of IPV.
Sprecher
Armand G
AG
Section of Emergency Medicine,University of Nebraska Medical Center, Omaha 68198-1150, USA.
Muelleman
Robert L
RL
Wadman
Michael C
MC
D016022:Case-Control Studies; D004636:Emergency Service, Hospital; D005188:False Negative Reactions; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D016571:Neural Networks, Computer; D011897:Random Allocation; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D012984:Software; D013179:Spouse Abuse / Q000175:diagnosis*