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

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Published on: October 24, 2019

Comparing Open-Access Database and Traditional Intensive Care Studies Using Machine Learning: Bibliometric Analysis

Yuhe Ke¹, Rui Yang², Nan Liu²

¹Division of Anesthesiology and Perioperative Medicine, Singapore General Hospital, Singapore, Singapore.

Journal of Medical Internet Research

|April 17, 2024

Summary

This summary is machine-generated.

Machine learning analysis reveals open-access databases (OADs) complement traditional intensive care unit (ICU) studies by focusing on predictive modeling. Integrating both OAD and traditional intensive care research offers a comprehensive future for ICU insights.

Keywords:

BERTopic MIMIC Medical Information Mart for Intensive Care critical care eICU machine learning natural language processing

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Published on: January 16, 2019

Area of Science:

Intensive care medicine
Bibliometrics
Machine learning applications

Background:

Traditional intensive care unit (ICU) research primarily uses randomized controlled trials.
Open-access databases (OADs) have emerged as a significant resource for contemporary research.
Machine learning (ML) facilitates trend analysis across large study volumes.

Purpose of the Study:

To conduct a bibliometric analysis comparing research trends and topics in traditional intensive care (TIC) studies versus those utilizing OADs.
To leverage ML for a comprehensive comparison of these two research methodologies.

Main Methods:

Utilized ML for analyzing publications from the Web of Science database.
Categorized studies into OAD and TIC groups, with OADs including MIMIC, eICU-CRD, AmsterdamUMCdb, HiRID, and Pediatric Intensive Care database.
Employed Uniform Manifold Approximation and Projection for corpus visualization and BERTopic for topic identification and categorization.

Main Results:

Analyzed 145,426 TIC articles and 1,301 OAD articles.
TIC studies showed exponential growth, peaking in 2021, while OAD studies have steadily increased since 2018.
Sepsis, ventilation research, and pediatric intensive care were prominent topics; TIC studies covered a broader research scope.

Conclusions:

OAD studies enhance traditional intensive care research by focusing on predictive modeling.
TIC studies provide essential qualitative data, complementing OAD findings.
Integrating both OAD and traditional intensive care approaches, alongside natural language processing, represents the future of ICU research and literature review.