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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