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|Title: ||Term Identification Methods for Consumer Health Vocabulary Development|
|Authors: ||Zeng, Qing T|
Browne, Allen C
|Keywords: ||Original Paper|
Consumer health information
natural language processing
|Issue Date: ||14-Mar-2007|
|Publisher: ||Gunther Eysenbach; Centre for Global eHealth Innovation, Toronto, Canada|
|Citation: ||Qing T Zeng, Tony Tse, Guy Divita, Alla Keselman, Jon Crowell, Allen C Browne, Sergey Goryachev, Long Ngo. Term Identification Methods for Consumer Health Vocabulary Development. J Med Internet Res 2007;9(1):e4 <URL: http://www.jmir.org/2007/1/e4/>|
|Abstract: ||[This item is a preserved copy and is not necessarily the most recent version. To view the current item, visit http://www.jmir.org/2007/1/e4/ ]
The development of consumer health information applications such as health education websites has motivated the research on consumer health vocabulary (CHV). Term identification is a critical task in vocabulary development. Because of the heterogeneity and ambiguity of consumer expressions, term identification for CHV is more challenging than for professional health vocabularies.
For the development of a CHV, we explored several term identification methods, including collaborative human review and automated term recognition methods.
A set of criteria was established to ensure consistency in the collaborative review, which analyzed 1893 strings. Using the results from the human review, we tested two automated methods—C-value formula and a logistic regression model.
The study identified 753 consumer terms and found the logistic regression model to be highly effective for CHV term identification (area under the receiver operating characteristic curve = 95.5%).
The collaborative human review and logistic regression methods were effective for identifying terms for CHV development.|
|Description: ||Reviewer: Patrick, Timothy|
|Rights: ||© Qing T Zeng, Tony Tse, Guy Divita, Alla Keselman, Jon Crowell, Allen C Browne, Sergey Goryachev, Long Ngo. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 14.03.2007. Except where otherwise noted, articles published in the Journal of Medical Internet Research are distributed under the terms of the Creative Commons Attribution License (http://www.creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited, including full bibliographic details and the URL (see "please cite as" above), and this statement is included.|
|Appears in Collections:||Volume 9 (2007)|
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