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01.03.2018 | Research | Sonderheft 1/2018 Open Access

BMC Medical Informatics and Decision Making 1/2018

Leveraging text skeleton for de-identification of electronic medical records

Zeitschrift:
BMC Medical Informatics and Decision Making > Sonderheft 1/2018
Autoren:
Yue-Shu Zhao, Kun-Li Zhang, Hong-Chao Ma, Kun Li

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.
Literatur
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