Using generative artificial intelligence in bibliometric analysis

10 years of research trends from the European Resuscitation congresses

Nino Fijačko (Author), Ruth Masterson Creber (Author), Benjamin S. Abella (Author), Primož Kocbek (Author), Špela Metličar (Author), Robert Greif (Author), Gregor Štiglic (Author)

Abstract

Aims: The aim of this study is to use generative artificial intelligence to perform bibliometric analysis on abstracts published at European Resuscitation Council (ERC) annual scientific congress and define trends in ERC guidelines topics over the last decade. Methods: In this bibliometric analysis, the WebHarvy software (SysNucleus, India) was used to download data from the Resuscitation journal’s website through the technique of web scraping. Next, the Chat Generative Pre-trained Transformer 4 (ChatGPT-4) application programming interface (Open AI, USA) was used to implement the multinomial classification of abstract titles following the ERC 2021 guidelines topics. Results: From 2012 to 2022 a total of 2491 abstracts have been published at ERC congresses. Published abstracts ranged from 88 (in 2020) to 368 (in 2015). On average, the most common ERC guidelines topics were Adult basic life support (50.1%), followed by Adult advanced life support (41.5%), while Newborn resuscitation and support of transition of infants at birth (2.1%) was the least common topic. The findings also highlight that the Basic Life Support and Adult Advanced Life Support ERC guidelines topics have the strongest co-occurrence to all ERC guidelines topics, where the Newborn resuscitation and support of transition of infants at birth (2.1%; 52/2491) ERC guidelines topic has the weakest co-occurrence. Conclusion: This study demonstrates the capabilities of generative artificial intelligence in the bibliometric analysis of abstract titles using the example of resuscitation medicine research over the last decade at ERC conferences using large language models.

Keywords

generative artificial intelligence;bibliometric analysis;congress;emergency medicine;European Resuscitation Council;

Data

Language:	English
Year of publishing:	2024
Typology:	1.03 - Short Scientific Article
Organization:	UM FZV - Faculty of Health Sciences
Publisher:	ELSEVIER
UDC:	004.8:616-083.98
COBISS:	187063811
ISSN:	2666-5204
Views:	0
Downloads:	1
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	generativna umetna inteligenca;bibliometrična analiza;kongres;urgentna medicina;Evropski svet za reanimacijo;
Type (COBISS):	Scientific work
Pages:	str. 1-5
Issue:	ǂVol. ǂ18, [article no.] 100584
Chronology:	2024
DOI:	10.1016/j.resplu.2024.100584
ID:	25420109