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 describe the development of artificial intelligence algorithms that use unstructured free-text in the electronic medical record—including notes from physicians, nurses, and social workers—to identify children who are suspected victims of physical abuse. Importantly, only the notes from time of first encounter (e.g.: birth, routine visit, sickness) to the last record before child protection team involvement were used. This allowed us to develop an algorithm using only information available prior to referral to the specialized child protection team. The study was performed in a multi-center referral pediatric hospital on patients screened for abuse within five different locations between 2015 and 2019. Of 1123 patients, 867 records were available after data cleaning and processing, and 55% were abuse-positive as determined by a multi-disciplinary team of clinical professionals. These electronic medical records were encoded with three natural language processing (NLP) algorithms—Bag of Words (BOW), Word Embeddings (WE), and Rules-Based (RB)—and used to train multiple neural network architectures. The BOW and WE encodings utilize the full free-text, while RB selects crucial phrases as identified by physicians. The best architecture was selected by average classification accuracy for the best performing model from each train-test split of a cross-validation experiment. Natural language processing coupled with neural networks detected cases of likely child abuse using only information available to clinicians prior to child protection team referral with average accuracy of 0.90±0.02 and average area under the receiver operator characteristic curve (ROC-AUC) 0.93±0.02 for the best performing Bag of Words models. The best performing rules-based models achieved average accuracy of 0.77±0.04 and average ROC-AUC 0.81±0.05, while a Word Embeddings strategy was severely limited by lack of representative embeddings. Importantly, the best performing model had a false positive rate of 8%, as compared to rates of 20% or higher in previously reported studies. This artificial intelligence approach can help screen patients for whom an abuse concern exists and streamline the identification of patients who may benefit from referral to a child protection team. Furthermore, this approach could be applied to develop computer-aided-diagnosis platforms for the challenging and often intractable problem of reliably identifying pediatric patients suffering from physical abuse.

Machine Learning Approaches for Electronic Health Records Phenotyping: A Methodical Review

“…The decreased performance at the Korean site is likely due to regional differences in the quality of silver-standard labels. Similar approaches utilizing deep learning approaches have been developed in [25].…”

“…The most common phenotypes are summarized in Figure 3 and include chronic conditions as well as adverse drug events. Social determinants of health such as marital status and homelessness were considered in 7 of the 106 articles [25][26][27][28][29][30][31], while more nuanced phenotypes such as disease severity (n = 5, 4.7%) [32][33][34][35][36] and disease subtypes (n = 19, 17.9%) [37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54] are emerging areas of focus. We also classified each phenotype as either binary (eg.…”

“…Among the methods used to train word embeddings, Word2vec [98] and Bidirectional Encoder Representations from Transformers (BERT) and variants [99][100][101][102] are the most frequently used (Supplementary Material Table S9). Word embeddings typically serve as the input layer to phenotyping algorithms using deep learning models and have also been used to account for ambiguous abbreviations and spelling errors in clinical notes [25,27,29,32,34,42,69,72,[74][75][76][77]83,[93][94][95][96][97][103][104][105][106][107][108][109][110][111][112][113][114].…”

“…33 of the 72 supervised learning articles employ deep learning, with 31 of these articles using word embeddings as the input layer. Feed-forward neural networks are the least frequently used architecture [25,32,108,111,118,133], while recurrent neural networks (RNNS), [27,34,59,69,[74][75][76]85,90,94,95,103,[105][106][107]109,110,123,126,130,134], convolutional neural networks (CNNs) [33,75,83,91,93,94,103,106,107,112,126,[134][135][136], and their variants are frequently used (Supplementary Material Table S12). RNNs are particularly well suited for sequential data from longitudinal EHRs.…”

Technology-Based Mental Health Interventions for Domestic Violence Victims Amid COVID-19

“…These combined insights suggest that greater governmental support and oversight is needed to ensure the healthy development of technology-based interventions for domestic violence victims. Considering that domestic violence victims often live with their abusers [ 69 ], advanced technological solutions, such as AI-enabled facial recognition, can be integrated into various interventions to ensure the content can only be accessed by the victims. Researchers could also use AI technologies, such as natural language processing, to analyze electronic health records to potentially identify victims’ susceptibility to mental health issues before such issues become chronic or permanent [ 70 , 71 ].…”