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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

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Published on: October 24, 2019

Models for predicting and explaining citation count of biomedical articles.

Lawrence D Fu¹, Constantin Aliferis

¹Vanderbilt University, Nashville, TN, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|November 13, 2008

Summary

Predicting the future impact of biomedical research is now possible. Computer models can forecast citation counts for scientific papers years in advance using publication data, aiding research assessment.

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Area of Science:

Bibliometrics and scientometrics
Biomedical informatics
Machine learning applications in science

Background:

Citation count is a key metric for evaluating biomedical research impact.
Citation data is only available years after publication, limiting timely assessment.
Predicting future citation counts at publication time is a significant challenge.

Purpose of the Study:

To develop and validate computer models for predicting long-term citation counts of biomedical publications.
To utilize only information available at the time of publication for accurate predictions.
To offer a method for early assessment of research impact.

Main Methods:

Development of predictive computer models using machine learning.
Integration of content-based features and bibliometric data.
Validation of model accuracy for predicting citation counts up to ten years post-publication.

Main Results:

Accurate prediction of future citation counts is feasible using machine learning models.
A combination of content-based and bibliometric features enhances prediction accuracy.
Models demonstrate reliable performance in forecasting long-term research impact.

Conclusions:

Machine learning models can effectively predict the long-term impact of biomedical publications.
Early prediction of citation counts enables timely evaluation of research significance.
The study provides insights into citation behavior and research impact assessment.