Leveraging text skeleton for de-identification of electronic medical records

Abstract

Background: De-identification is the first step to use these records for data processing or further medical investigations in electronic medical records. Consequently, a reliable automated de-identification system would be of high value. Methods: In this paper, a method of combining text skeleton and recurrent neural network is proposed to solve the problem of de-identification. Text skeleton is the general structure of a medical record, which can help neural networks to learn better. Results: We evaluated our method on three datasets involving two English datasets from i2b2 de-identification challenge and a Chinese dataset we annotated. Empirical results show that the text skeleton based method we proposed can help the network to recognize protected health information. Conclusions: The comparison between our method and state-of-the-art frameworks indicates that our method achieves high performance on the problem of medical record de-identification.