LD Fu et al., A Comparison of Impact Factor, Clinical Query Filters, and Pattern Recognition Query Filters in Terms of Sensitivity to Topic. MEDINFO 2007

[Eugene Garfield]

Fu, LD; Wang, L; Aphinyanagphongs, Y; Aliferis, CF. 2007. A Comparison of 
Impact Factor, Clinical Query Filters, and Pattern Recognition Query Filters in 
Terms of Sensitivity to Topic. MEDINFO 2007: PROCEEDINGS OF THE 12TH 
WORLD CONGRESS ON HEALTH (MEDICAL) INFORMATICS, PTS 1 AND 2 - 
BUILDING SUSTAINABLE HEALTH SYSTEMS 129: 716-720. edited by Kuhn, KA; 
Warren, JR; Leong, TY. AMSTERDAM, I O S PRESS presented at 12th World 
Congress on Health (Medical) Informatics in Brisbane, AUSTRALIA, AUG 20-24, 
2007.

Author Full Name(s): Fu, Lawrence D.; Wang, Lily; Aphinyanagphongs, 
Yindalon; Aliferis, Constantin F.

Book series title: Studies in Health Technology and Informatics
Language: English
Document Type: Proceedings Paper
KeyWords Plus: MEDLINE

Abstract: Evaluating journal quality and finding high-quality articles in the 
biomedical literature are challenging information retrieval tasks. The most widely 
used method for journal evaluation is impact factor, while novel approaches for 
finding articles are PubMed's clinical query filters and machine learning-based 
filter models. The related literature has focused on the average behavior of 
these methods over all topics. The present study evaluates the variability of 
these approaches for different topics. We find that impact factor and clinical 
query filters are unstable for different topics while a topic-specific impact 
factor and machine learning-based filter models appear more robust. Thus when 
using the less stable methods for a specific topic, researchers should realize 
that their performance may diverge from expected average performance. Better 
yet, the more stable methods should be preferred whenever applicable.

Addresses: [Fu, Lawrence D.; Aphinyanagphongs, Yindalon; Aliferis, Constantin 
F.] Vanderbilt Univ, Dept Biomed Informat, Nashville, TN 37235 USA
Reprint Address: Fu, LD, Vanderbilt Univ, Dept Biomed Informat, Nashville, TN 
37235 USA.

ISSN: 0926-9630
ISBN: 978-1-58603-774-1