Using generative artificial intelligence in bibliometric analysis: 10 years of research trends from the European Resuscitation Congresses
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,...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | Resuscitation Plus |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666520424000353 |
| _version_ | 1797299723416109056 |
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| author | Nino Fijačko Ruth Masterson Creber Benjamin S. Abella Primož Kocbek Špela Metličar Robert Greif Gregor Štiglic |
| author_facet | Nino Fijačko Ruth Masterson Creber Benjamin S. Abella Primož Kocbek Špela Metličar Robert Greif Gregor Štiglic |
| author_sort | Nino Fijačko |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T22:53:48Z |
| format | Article |
| id | doaj.art-eed4782ff41e467b9c5edc6bcd69ce33 |
| institution | Directory Open Access Journal |
| issn | 2666-5204 |
| language | English |
| last_indexed | 2024-03-07T22:53:48Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Resuscitation Plus |
| spelling | doaj.art-eed4782ff41e467b9c5edc6bcd69ce332024-02-23T05:00:51ZengElsevierResuscitation Plus2666-52042024-06-0118100584Using generative artificial intelligence in bibliometric analysis: 10 years of research trends from the European Resuscitation CongressesNino Fijačko0Ruth Masterson Creber1Benjamin S. Abella2Primož Kocbek3Špela Metličar4Robert Greif5Gregor Štiglic6University of Maribor, Faculty of Health Sciences, Maribor, Slovenia; ERC Research Net, Niels, Belgium; Maribor University Medical Centre, Maribor, Slovenia; Corresponding author at: Žitna ulica 15, 2000 Maribor, University of Maribor, Faculty of Health Sciences, Maribor, Slovenia.Columbia University School of Nursing, New York, NY, USACenter for Resuscitation Science and Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, USAUniversity of Maribor, Faculty of Health Sciences, Maribor, Slovenia; University of Ljubljana, Faculty of Medicine, Ljubljana, SloveniaUniversity of Maribor, Faculty of Health Sciences, Maribor, Slovenia; Medical Dispatch Centre Maribor, University Clinical Centre Ljubljana, Ljubljana, SloveniaERC Research Net, Niels, Belgium; University of Bern, Bern, Switzerland; School of Medicine, Sigmund Freud University Vienna, Vienna, AustriaUniversity of Maribor, Faculty of Health Sciences, Maribor, Slovenia; University of Maribor, Faculty of Electrical Engineering and Computer Science, Maribor, Slovenia; Usher Institute, University of Edinburgh, Edinburgh, United KingdomAims: 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.http://www.sciencedirect.com/science/article/pii/S2666520424000353Emergency medicineEuropean Resuscitation CouncilCongressBibliometrics analysisGenerative artificial intelligence |
| spellingShingle | Nino Fijačko Ruth Masterson Creber Benjamin S. Abella Primož Kocbek Špela Metličar Robert Greif Gregor Štiglic Using generative artificial intelligence in bibliometric analysis: 10 years of research trends from the European Resuscitation Congresses Resuscitation Plus Emergency medicine European Resuscitation Council Congress Bibliometrics analysis Generative artificial intelligence |
| title | Using generative artificial intelligence in bibliometric analysis: 10 years of research trends from the European Resuscitation Congresses |
| title_full | Using generative artificial intelligence in bibliometric analysis: 10 years of research trends from the European Resuscitation Congresses |
| title_fullStr | Using generative artificial intelligence in bibliometric analysis: 10 years of research trends from the European Resuscitation Congresses |
| title_full_unstemmed | Using generative artificial intelligence in bibliometric analysis: 10 years of research trends from the European Resuscitation Congresses |
| title_short | Using generative artificial intelligence in bibliometric analysis: 10 years of research trends from the European Resuscitation Congresses |
| title_sort | using generative artificial intelligence in bibliometric analysis 10 years of research trends from the european resuscitation congresses |
| topic | Emergency medicine European Resuscitation Council Congress Bibliometrics analysis Generative artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2666520424000353 |
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