State-of-the-art anonymization of medical records using an iterative machine learning framework

Objective: The anonymization of medical records is of great importance in the human life sciences because a de-identified text can be made publicly available for non-hospital researchers as well, to facilitate research on human diseases. Here the authors have developed a de-identification model that can successfully remove personal health information (PHI) from discharge records to make them conform to the guidelines of the Health Information Portability and Accountability Act. Design: We introduce here a novel, machine learning-based iterative Named Entity Recognition approach intended for use on semi-structured documents like discharge records. Our method identifies PHI in several steps. First, it labels all entities whose tags can be inferred from the structure of the text and it then utilizes this information to find further PHI phrases in the flow text parts of the document. Measurements: Following the standard evaluation method of the first Workshop on Challenges in Natural Language Processing for Clinical Data, we used token-level Precision, Recall and F-beta=1 measure metrics for evaluation. Results: Our system achieved outstanding accuracy on the standard evaluation dataset of the de-identification challenge, with an F measure of 99.7534% for the best submitted model. Conclusion: We can say that our system is competitive with the current state-of-the-art solutions, while we describe here several techniques that can be beneficial in other tasks that need to handle structured documents such as clinical records.