Identifying child abuse through text mining and machine learning

In this paper, we describe how we used text mining and analysis to identify and predict cases of child abuse in a public health institution. Such institutions in the Netherlands try to identify and prevent different kinds of abuse. A significant part of the medical data that the institutions have on children is unstructured, found in the form of free text notes. We explore whether these consultation data contain meaningful patterns to determine abuse. Then we train machine learning models on cases of abuse as determined by over 500 child specialists from a municipality in The Netherlands. The resulting model achieves a high score in classifying cases of possible abuse. We methodologically evaluate and compare the performance of the classifiers. We then describe our implementation of the decision support API at a municipality in the Netherlands.