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Application of a feature extraction and normalization method to improve research evaluation across clinical disciplines.

Authors
  • Liu, Rui1
  • Liu, Qian2
  • Shi, Jianwei3
  • Yu, Wenya3
  • Gong, Xin4
  • Chen, Ning5
  • Yang, Yan2
  • Huang, Jiaoling3
  • Wang, Zhaoxin3, 6
  • 1 Shanghai Tenth People's Hospital of Tongji University, Shanghai, China. , (China)
  • 2 School of Economics and Management, Tongji University, Shanghai, China. , (China)
  • 3 School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China. , (China)
  • 4 Shanghai East Hospital Affiliated to Tongji University School of Medicine, Shanghai, China. , (China)
  • 5 School of Medicine, Tongji University, Shanghai, China. , (China)
  • 6 General Practice Center, Nanhai Hospital, Southern Medical University, Foshan, China. , (China)
Type
Published Article
Journal
Annals of Translational Medicine
Publisher
AME Publishing Company
Publication Date
Oct 01, 2021
Volume
9
Issue
20
Pages
1580–1580
Identifiers
DOI: 10.21037/atm-21-5046
PMID: 34790786
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

To deal with the large disparity across disciplines using impact factor, which is widely used in hospitals and has recently come under attack for distorting good scientific practices, we propose a set of systematic methods to improve the equality of research evaluations of various clinical disciplines. We used bibliometric information on 18 clinical disciplines from 2016 to 2018. We first sought to clarify disciplinary characteristics with the aim of identifying the characteristic fields for each clinical discipline, and we constructed a keyword database. To minimize the disparity across various clinical disciplines, we used normalized evaluation, referring to the calculation of the normalized coefficient of a specific discipline, to enable a relatively clear evaluation across different disciplines. Feature extraction was performed, and over 700,000 journals were retrieved each year. Using this information, the journal correlation coefficient was calculated. From 2016 to 2018, oncology had the largest normalized coefficient (0.133, 0.136, 0.146 respectively), which reflects the highest correlation between the characteristic journals of the discipline. The findings showed a clear distinction in journal coverage and journal correlations for different disciplines. The new evaluation indicator and normalized process measure different features of disciplines, providing a basis for the further balancing of evaluations, and considering differences across disciplines. 2021 Annals of Translational Medicine. All rights reserved.

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