Identifying intentional injuries among children and adolescents based on Machine Learning

Abstract

Background Compared to other studies, the injury monitoring of Chinese children and adolescents has captured a low level of intentional injuries on account of self-harm/suicide and violent attacks. Intentional injuries in children and adolescents have not been apparent from the data. It is possible that there has been a misclassification of existing intentional injuries, and there is a lack of research literature on the misclassification of intentional injuries. This study aimed to discuss the feasibility of discriminating the intention of injury based on Machine Learning (ML) modelling and provided ideas for understanding whether there was a misclassification of intentional injuries. Methods Information entropy was used to determine the correlation between variables and the intention of injury, and Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Adaboost algorithms and Deep Neural Networks (DNN) were used to create an intention of injury discrimination model. The models were compared by comprehensively testing the discrimination effect to determine stability and consistency. Results For the area under the ROC curve with different intentions of injuries, the NB model was 0.891, 0.880, and 0.897, respectively; the DT model was 0.870, 0.803, and 0.871, respectively; the RF model was 0.850, 0.809, and 0.845, respectively; the Adaboost model was 0.914, 0.846, and 0.914, respectively; the DNN model was 0.927, 0.835, and 0.934, respectively. In a comprehensive comparison of the five models, DNN and Adaboost models had higher values for the determination of the intention of injury. A discrimination of cases with unclear intentions of injury showed that on average, unintentional injuries, violent attacks, and self-harm/suicides accounted for 86.57%, 6.81%, and 6.62%, respectively. Conclusion It was feasible to use the ML algorithm to determine the injury intention of children and adolescents. The research suggested that the DNN and Adaboost models had higher values for the determination of the intention of injury. This study could build a foundation for transforming the model into a tool for rapid diagnosis and excavating potential intentional injuries of children and adolescents by widely collecting the influencing factors, extracting the influence variables characteristically, reducing the complexity and improving the performance of the models in the future.

Evaluation of intentional and unintentional injuries in children and adolescents with trauma scoring systems

“…This was thought to be related to the fact that adolescents are more prone to violence. In China, fractures developed in 8.81% of unintentional injuries and 2.58% of intentional injuries in patients aged <18 years [1]. In this study, the incidence of fracture was significantly higher in the unintentional injury group.…”

“…More than two-thirds (67.74%) of children and adolescents injured in China between 2006 and 2017 were males [1]. In low-and middle-income countries, 64.70% of injured children aged 0-12 years were boys [13].…”

“…Nearly half of the intentional injuries occurred to the head and neck, while unintentional injuries generally occurred to the upper and lower extremities [24]. Half of the unintentional injuries (50.44%) in children and adolescents in China were to the extremities, while the head (39.7%) was the most frequent injury site in intentional injuries [1]. In this study, while the head and neck injury rate was higher in intentional injuries, extremity injuries were higher in unintentional injuries.…”

“…Children and adolescents are populations at high risk of injury [1]. Every year, approximately 950,000 children under the age of 18 years die as a result of injuries and assaults, and many more suffer injuries that require treatment in hospitals and may even result in disability [2].…”

BS-SC Model: A Novel Method for Predicting Child Abuse Using Borderline-SMOTE Enabled Stacking Classifier

“…Yin et al evaluated various Machine Learning models by utilizing data from the Chinese National Injury Surveillance System (NISS). Deep Neural Networks and AdaBoost models were more successful than the others in classifying injuries [30].…”