Text categorization models for identifying unproven cancer treatments on the web

Yin Aphinyanaphongs, Constantin Aliferis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The nature of the internet as a non-peer-reviewed (and largely unregulated) publication medium has allowed wide-spread promotion of inaccurate and unproven medical claims in unprecedented scale. Patients with conditions that are not currently fully treatable are particularly susceptible to unproven and dangerous promises about miracle treatments. In extreme cases, fatal adverse outcomes have been documented. Most commonly, the cost is financial, psychological, and delayed application of imperfect but proven scientific modalities. To help protect patients, who may be desperately ill and thus prone to exploitation, we explored the use of machine learning techniques to identify web pages that make unproven claims. This feasibility study shows that the resulting models can identify web pages that make unproven claims in a fully automatic manner, and substantially better than previous web tools and state-of-the-art search engine technology.

Original languageEnglish (US)
Title of host publicationMEDINFO 2007 - Proceedings of the 12th World Congress on Health (Medical) Informatics
Subtitle of host publicationBuilding Sustainable Health Systems
Pages968-972
Number of pages5
Volume129
StatePublished - Dec 1 2007
Event12th World Congress on Medical Informatics, MEDINFO 2007 - Brisbane, QLD, Australia
Duration: Aug 20 2007Aug 24 2007

Other

Other12th World Congress on Medical Informatics, MEDINFO 2007
CountryAustralia
CityBrisbane, QLD
Period8/20/078/24/07

Fingerprint

Oncology
Websites
Search Engine
Fatal Outcome
Feasibility Studies
Search engines
World Wide Web
Internet
Learning systems
Publications
Neoplasms
Psychology
Technology
Costs and Cost Analysis
Therapeutics
Costs
Machine Learning

Keywords

  • information storage and retrieval
  • internet
  • medical informatics
  • neoplasms
  • text categorization

Cite this

Aphinyanaphongs, Y., & Aliferis, C. (2007). Text categorization models for identifying unproven cancer treatments on the web. In MEDINFO 2007 - Proceedings of the 12th World Congress on Health (Medical) Informatics: Building Sustainable Health Systems (Vol. 129, pp. 968-972)

Text categorization models for identifying unproven cancer treatments on the web. / Aphinyanaphongs, Yin; Aliferis, Constantin.

MEDINFO 2007 - Proceedings of the 12th World Congress on Health (Medical) Informatics: Building Sustainable Health Systems. Vol. 129 2007. p. 968-972.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Aphinyanaphongs, Y & Aliferis, C 2007, Text categorization models for identifying unproven cancer treatments on the web. in MEDINFO 2007 - Proceedings of the 12th World Congress on Health (Medical) Informatics: Building Sustainable Health Systems. vol. 129, pp. 968-972, 12th World Congress on Medical Informatics, MEDINFO 2007, Brisbane, QLD, Australia, 8/20/07.
Aphinyanaphongs Y, Aliferis C. Text categorization models for identifying unproven cancer treatments on the web. In MEDINFO 2007 - Proceedings of the 12th World Congress on Health (Medical) Informatics: Building Sustainable Health Systems. Vol. 129. 2007. p. 968-972
Aphinyanaphongs, Yin ; Aliferis, Constantin. / Text categorization models for identifying unproven cancer treatments on the web. MEDINFO 2007 - Proceedings of the 12th World Congress on Health (Medical) Informatics: Building Sustainable Health Systems. Vol. 129 2007. pp. 968-972
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