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Leveraging text skeleton for de-identification of electronic medical records

Overview of attention for article published in BMC Medical Informatics and Decision Making, March 2018
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1 tweeter

Citations

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Title
Leveraging text skeleton for de-identification of electronic medical records
Published in
BMC Medical Informatics and Decision Making, March 2018
DOI 10.1186/s12911-018-0598-6
Pubmed ID
Authors

Yue-Shu Zhao, Kun-Li Zhang, Hong-Chao Ma, Kun Li

Abstract

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. 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. 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. 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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Other 4 19%
Student > Master 4 19%
Student > Ph. D. Student 4 19%
Researcher 3 14%
Student > Bachelor 2 10%
Other 2 10%
Unknown 2 10%
Readers by discipline Count As %
Computer Science 8 38%
Medicine and Dentistry 2 10%
Engineering 2 10%
Psychology 1 5%
Social Sciences 1 5%
Other 3 14%
Unknown 4 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 March 2018.
All research outputs
#11,297,371
of 12,698,622 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,117
of 1,143 outputs
Outputs of similar age
#238,692
of 274,011 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#3
of 3 outputs
Altmetric has tracked 12,698,622 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,143 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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