Using deep learning and natural language processing models to detect child physical abuse.

Abstract

BACKGROUND\nThe recognition of child physical abuse can be challenging and often requires a multidisciplinary assessment. Deep learning models, based on clinical characteristics, laboratory studies, and imaging findings, were developed to facilitate unbiased identification of children who may have been abused.\n\n\nMETHODS\nLevel 1 pediatric trauma center registry data from 1/1/2010-1/31/2020 were queried for abused children and matched participants with non-abusive trauma. Observations were de-identified and divided into training and validation sets. Model 1 used patient demographics (age, gender, and insurance type) and clinical characteristics (vital signs, shock index pediatric age-adjusted, Glasgow Coma Score, lactate, base deficit, and international normalized ratio). Model 2 used the same features as Model 1, but with the text of the radiology reports of head computed tomography, brain MRIs, and skeletal surveys. Google s latest BERT Natural Language Processing (NLP) model, which was pre-trained on a large corpus, was used for fine-tuning Model 2. Accuracy, sensitivity, specificity, F1 scores, and positive predictive values were used to assess performance.\n\n\nRESULTS\nOf 1,312 patients, 737 (56.2%) were abused. Model 1 had an accuracy of 86.3%, sensitivity of 87.2%, specificity of 85.1%, F1 score of 0.86, and positive predictive value (PPV) of 88.7% for the validation set with an area under the receiver Operating Curve (ROC AUC) of 0.86. NLP based Model 2 had an accuracy of 93.4%, sensitivity 92.5%, specificity of 94.6%, F1 score of 0.93, and PPV of 95.9% for the validation set, with a ROC AUC of 0.94. Most features had weak individual correlations with abuse (r\xa0<\xa00.3).\n\n\nCONCLUSIONS\nDeep learning models accurately distinguished child physical abuse from non-abuse, and NLP further improved the accuracy of the models. Such models could be developed to run in real-time in the electronic medical record and alert clinicians when certain criteria are met, which would prompt them to pursue the diagnosis of abuse.\n\n\nLEVEL OF EVIDENCE\nIII STUDY TYPE: Diagnostic.