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

TitleMachine learning approach for automatic quality criteria detection of health web pages.
Publication TypeJournal Article
Year of Publication2007
AuthorsGaudinat, A, Grabar, N, Boyer, C
JournalStudies in health technology and informatics
Volume129
IssuePt 1
Pagination705-9
Date Published2007
ISSN0926-9630
KeywordsArtificial Intelligence, Codes of Ethics, Health, Humans, Information Services, Internet, Medical Informatics, Natural Language Processing, Quality Control
Abstract

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.

Alternate JournalStud Health Technol Inform
PubMed ID17911808