Machine learning approach for automatic quality criteria detection of health web pages.

Machine learning approach for automatic quality criteria detection of health web pages. Research Abstract Details 

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Machine learning approach for automatic quality criteria detection of health web pages. Abstract Text:

Arnaud Gaudinat,Natalia Grabar, Boyer,

The number of medical websites is constantly growing [1]. Owing to the open nature of the Web, the reliability of information available on the Web is uneven. Internet users are overwhelmed by the quantity of information available on the Web. The situation is even more critical in the medical area, as the content proposed by health websites can have a direct impact on the users' well being. One way to control the reliability of health websites is to assess their quality and to make this assessment available to users. The HON Foundation has defined a set of eight ethical principles. HON's experts are working in order to manually define whether a given website complies with s the required principles. As the number of medical websites is constantly growing, manual expertise becomes insufficient and automatic systems should be used in order to help medical experts. In this paper we present the design and the evaluation of an automatic system conceived for the categorisation of medical and health documents according to he HONcode ethical principles. A first evaluation shows promising results. Currently the system shows 0.78 micro precision and 0.73 F-measure, with 0.06 errors.

Machine learning approach for automatic quality criteria detection of health web pages. Publishing Authors By Initials

A Gaudinat,N Grabar,C Boyer,

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Machine learning approach for automatic quality criteria detection of health web pages. Journal Published:

PUBLICATION TYPE: Research Support, Non-U.S. Gov

Journal: Medinfo. MEDINFO

VOLUME: 12

Page Numbers: 705-9

Journal Abbreviation: Medinfo

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DAY: 28

MONTH: 11

YEAR: 2007

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LANGUAGE: eng

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Grant and Affiliation Information for Machine learning approach for automatic quality criteria detection of health web pages.

AFFILIATION: Health on the Net Foundation, HUG/DIM, Geneva 14, Switzerland. arnaud.gaudinat@healthonnet.org

Country: Netherlands

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MEDLINETA: Medinfo

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