Fu LD, Aphinyanaphongs Y, Wang LL, Aliferis CF "A comparison of evaluation metrics for biomedical journals, articles, and websites in terms of sensitivity to topic " JASIST 44(4):587-594, August 2011

Eugene Garfield garfield at CODEX.CIS.UPENN.EDU
Thu Aug 25 16:43:33 EDT 2011


E-mail: lawrence.fu at nyumc.org


TITLE  :A comparison of evaluation metrics for biomedical journals, articles, 
and websites in terms of sensitivity to topic  

Author(s): Fu, LD (Fu, Lawrence D.)1; Aphinyanaphongs, Y 
(Aphinyanaphongs, Yindalon)1; Wang, LL (Wang, Lily)2; Aliferis, CF 
(Aliferis, Constantin F.)1  

Source: JOURNAL OF BIOMEDICAL INFORMATICS  Volume: 44   Issue: 4   
Pages: 587-594   DOI: 10.1016/j.jbi.2011.03.006   Published: AUG 2011  

Times Cited: 0 (from Web of Science)  

Cited References: 22 [ view related records ]     Citation Map      

Abstract: 
Evaluating the biomedical literature and health-related websites for quality 
are challenging information retrieval tasks. Current commonly used methods 
include impact factor for journals, PubMed's clinical query filters and machine 
learning-based filter models for articles, and PageRank for websites. Previous 
work has focused on the average performance of these methods without 
considering the topic, and it is unknown how performance varies for specific 
topics or focused searches. Clinicians, researchers, and users should be 
aware when expected performance is not achieved for specific topics. The 
present work analyzes the behavior of these methods for a variety of topics. 
Impact factor, clinical query filters, and PageRank vary widely across 
different topics while a topic-specific impact factor and machine learning-
based filter models are more stable. The results demonstrate that a method 
may perform excellently on average but struggle when used on a number of 
narrower topics. Topic-adjusted metrics and other topic robust methods have 
an advantage in such situations. Users of traditional topic-sensitive metrics 
should be aware of their limitations. (C) 2011 Elsevier Inc. All rights 
reserved.  

Document Type: Article  
Language: English  

Author Keywords: Information retrieval; Machine learning; PageRank; 
Journal impact factor; Topic-sensitivity; Bibliometrics  
KeyWords Plus: IMPACT FACTORS; PAGERANK; SEARCH; LINK  

Reprint Address: Fu, LD (reprint author), NYU, Med Ctr, Ctr Hlth Informat & 
Bioinformat, 227 E 30th St,7th Floor, New York, NY 10016 USA  
Addresses: 
1. NYU, Med Ctr, Ctr Hlth Informat & Bioinformat, New York, NY 10016 USA 
2. Vanderbilt Univ, Sch Med, Dept Biostat, Nashville, TN 37232 USA  

E-mail Address: 
lawrence.fu at nyumc.org 
yin.a at nyumc.org
lily.wang at vanderbilt.edu
constantin.aliferis at nyumc.org 

Funding: 
Funding Agency Grant Number 
  R56 LM007948-04A1  
  1UL1RR029893  
 
[Show funding text][Hide funding text]   

The authors gratefully acknowledge support from Grants R56 LM007948-
04A1 and 1UL1RR029893.
 
Publisher: ACADEMIC PRESS INC ELSEVIER SCIENCE, 525 B ST, STE 1900, 
SAN DIEGO, CA 92101-4495 USA  
Web of Science Category: Computer Science, Interdisciplinary Applications; 
Medical Informatics  
Subject Category: Computer Science; Medical Informatics  
IDS Number: 800OO  
ISSN: 1532-0464  

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