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Automatic symptom name normalization in clinical records of traditional Chinese medicine

Overview of attention for article published in BMC Bioinformatics, January 2010
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1 tweeter

Citations

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31 Mendeley
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Title
Automatic symptom name normalization in clinical records of traditional Chinese medicine
Published in
BMC Bioinformatics, January 2010
DOI 10.1186/1471-2105-11-40
Pubmed ID
Authors

Yaqiang Wang, Zhonghua Yu, Yongguang Jiang, Kaikuo Xu, Xia Chen

Abstract

In recent years, Data Mining technology has been applied more than ever before in the field of traditional Chinese medicine (TCM) to discover regularities from the experience accumulated in the past thousands of years in China. Electronic medical records (or clinical records) of TCM, containing larger amount of information than well-structured data of prescriptions extracted manually from TCM literature such as information related to medical treatment process, could be an important source for discovering valuable regularities of TCM. However, they are collected by TCM doctors on a day to day basis without the support of authoritative editorial board, and owing to different experience and background of TCM doctors, the same concept might be described in several different terms. Therefore, clinical records of TCM cannot be used directly to Data Mining and Knowledge Discovery. This paper focuses its attention on the phenomena of "one symptom with different names" and investigates a series of metrics for automatically normalizing symptom names in clinical records of TCM.

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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 3%
Unknown 30 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 35%
Researcher 6 19%
Student > Doctoral Student 5 16%
Student > Bachelor 2 6%
Student > Postgraduate 2 6%
Other 5 16%
Readers by discipline Count As %
Medicine and Dentistry 12 39%
Computer Science 9 29%
Social Sciences 2 6%
Agricultural and Biological Sciences 2 6%
Pharmacology, Toxicology and Pharmaceutical Science 1 3%
Other 4 13%
Unknown 1 3%

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 26 June 2011.
All research outputs
#10,995,610
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#4,217
of 4,576 outputs
Outputs of similar age
#10,517,264
of 11,793,681 outputs
Outputs of similar age from BMC Bioinformatics
#4,217
of 4,581 outputs
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