Document Detail

Text categorization models for identifying unproven cancer treatments on the web.
MedLine Citation:
PMID:  17911859     Owner:  NLM     Status:  MEDLINE    
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.
Yin Aphinyanaphongs; Constantin Aliferis
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural    
Journal Detail:
Title:  Studies in health technology and informatics     Volume:  129     ISSN:  0926-9630     ISO Abbreviation:  Stud Health Technol Inform     Publication Date:  2007  
Date Detail:
Created Date:  2007-10-03     Completed Date:  2007-11-02     Revised Date:  2008-07-10    
Medline Journal Info:
Nlm Unique ID:  9214582     Medline TA:  Stud Health Technol Inform     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  968-72     Citation Subset:  T    
Department of Biomedical Informatics, Vanderbilt University School of Medicine, Vanderbilt Ingram Cancer Center, Nashville, TN, USA.
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MeSH Terms
Artificial Intelligence*
Feasibility Studies
Information Services / standards
Information Storage and Retrieval
Neoplasms / therapy*
ROC Curve
Grant Support
LM007948-01/LM/NLM NIH HHS; LM007948-02/LM/NLM NIH HHS

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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