System participating in the Meddocan task (2019).
Medical records contain relevant information about patients, which can be beneficial to improving healthcare and research in the clinical domain. Due to this, there is a growing interest in developing automatic methods to extract and exploit the information from medical records. However, medical records also contain protected health information about patients. To protect the confidentiality and privacy of patients, this sensitive information should be removed prior to any processing of these documents. In this paper, we describe an architecture for the detection and identification of protected health information from medical records. The architecture is composed of two bidirectional Long ShortTerm Memory layers and a final layer based on Conditional Random Fields. Our system participated in the Meddocan shared task, obtaining a micro-F1 of 93.22%
Dernoncourt, F., Lee, J.Y., Szolovits, P.: Neuroner: an easy-to-use program for named-entity recognition based on neural networks. arXiv preprint arXiv:1705.05487 (2017)