User Centred Quality Health Information Provision: Benefits and Challenges
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 6 - Volume 06
Evaluating the Quality of Health Web Sites: Developing a Validation Method and Rating Instrument
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 6 - Volume 06
Developing practical automatic metadata assignment and evaluation tools for internet resources
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Methodological Review: Empirical distributional semantics: Methods and biomedical applications
Journal of Biomedical Informatics
Semantic Space models for classification of consumer webpages on metadata attributes
Journal of Biomedical Informatics
Hi-index | 0.00 |
The continual growth of the World Wide Web presents the (also growing) population of health information seekers with the challenge of finding reliable information that is appropriate to their needs. Metadata about consumer health websites can provide a guide for end users and domain-specific search tools. In this paper we present and demonstrate a method for automatically inferring a non-trivial metadata attribute that has been encoded for breast cancer websites: whether the site is 'medical' or 'supportive' in tone. We induce decision trees to distinguish Medical vs. Supportive sites based on feature vectors of word co-occurrence patterns, founded in a semantic space model called Hyperspace Analog to Language (HAL). We achieve 82% (95% CI: 74% to 91%) classification accuracy. This should already be a useful capability for human metadata coders or to support on-the-fly queries, and it inspires us to further investigate metadata classifiers based on HAL features.