Deep Neural Networks for Inverse De-Identification of Medical Case Narratives in Reports of Suspected Adverse Drug Reactions

Abstract: Medical research requires detailed and accurate information on individual patients. This is especially so in the context of pharmacovigilance which amongst others seeks to identify previously unknown adverse drug reactions. Here, the clinical stories are often the starting point for assessing whether there is a causal relationship between the drug and the suspected adverse reaction. Reliable automatic de-identification of medical case narratives could allow to share this patient data without compromising the patient’s privacy. Current research on de-identification focused on solving the task of labelling the tokens in a narrative with the class of sensitive information they belong to. In this Master’s thesis project, we explore an inverse approach to the task of de-identification. This means that de-identification of medical case narratives is instead understood as identifying tokens which do not need to be removed from the text in order to ensure patient confidentiality. Our results show that this approach can lead to a more reliable method in terms of higher recall. We achieve a recall of sensitive information of 99.1% while the precision is kept above 51% for the 2014-i2b2 benchmark data set. The model was also fine-tuned on case narratives from reports of suspected adverse drug reactions, where a recall of sensitive information of more than 99% was achieved. Although the precision was only at a level of 55%, which is lower than in comparable systems, an expert could still identify information which would be useful for causality assessment in pharmacovigilance in most of the case narratives which were de-identified with our method. In more than 50% of the case narratives no information useful for causality assessment was missing at all.