TY - JOUR
T1 - Comprehensive overview of the anesthesiology research landscape
T2 - A machine Learning Analysis of 737 NIH-funded anesthesiology primary Investigator's publication trends
AU - Ghanem, Marc
AU - Espinosa, Camilo
AU - Chung, Philip
AU - Reincke, Momsen
AU - Harrison, Natasha
AU - Phongpreecha, Thanaphong
AU - Shome, Sayane
AU - Saarunya, Geetha
AU - Berson, Eloise
AU - James, Tomin
AU - Xie, Feng
AU - Shu, Chi Hung
AU - Hazra, Debapriya
AU - Mataraso, Samson
AU - Kim, Yeasul
AU - Seong, David
AU - Chakraborty, Dipro
AU - Studer, Manuel
AU - Xue, Lei
AU - Marić, Ivana
AU - Chang, Alan L.
AU - Tjoa, Erico
AU - Gaudillière, Brice
AU - Tawfik, Vivianne L.
AU - Mackey, Sean
AU - Aghaeepour, Nima
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/4/15
Y1 - 2024/4/15
N2 - Background: Anesthesiology plays a crucial role in perioperative care, critical care, and pain management, impacting patient experiences and clinical outcomes. However, our understanding of the anesthesiology research landscape is limited. Accordingly, we initiated a data-driven analysis through topic modeling to uncover research trends, enabling informed decision-making and fostering progress within the field. Methods: The easyPubMed R package was used to collect 32,300 PubMed abstracts spanning from 2000 to 2022. These abstracts were authored by 737 Anesthesiology Principal Investigators (PIs) who were recipients of National Institute of Health (NIH) funding from 2010 to 2022. Abstracts were preprocessed, vectorized, and analyzed with the state-of-the-art BERTopic algorithm to identify pillar topics and trending subtopics within anesthesiology research. Temporal trends were assessed using the Mann-Kendall test. Results: The publishing journals with most abstracts in this dataset were Anesthesia & Analgesia 1133, Anesthesiology 992, and Pain 671. Eight pillar topics were identified and categorized as basic or clinical sciences based on a hierarchical clustering analysis. Amongst the pillar topics, “Cells & Proteomics” had both the highest annual and total number of abstracts. Interestingly, there was an overall upward trend for all topics spanning the years 2000–2022. However, when focusing on the period from 2015 to 2022, topics “Cells & Proteomics” and “Pulmonology” exhibit a downward trajectory. Additionally, various subtopics were identified, with notable increasing trends in “Aneurysms”, “Covid 19 Pandemic”, and “Artificial intelligence & Machine Learning”. Conclusion: Our work offers a comprehensive analysis of the anesthesiology research landscape by providing insights into pillar topics, and trending subtopics. These findings contribute to a better understanding of anesthesiology research and can guide future directions.
AB - Background: Anesthesiology plays a crucial role in perioperative care, critical care, and pain management, impacting patient experiences and clinical outcomes. However, our understanding of the anesthesiology research landscape is limited. Accordingly, we initiated a data-driven analysis through topic modeling to uncover research trends, enabling informed decision-making and fostering progress within the field. Methods: The easyPubMed R package was used to collect 32,300 PubMed abstracts spanning from 2000 to 2022. These abstracts were authored by 737 Anesthesiology Principal Investigators (PIs) who were recipients of National Institute of Health (NIH) funding from 2010 to 2022. Abstracts were preprocessed, vectorized, and analyzed with the state-of-the-art BERTopic algorithm to identify pillar topics and trending subtopics within anesthesiology research. Temporal trends were assessed using the Mann-Kendall test. Results: The publishing journals with most abstracts in this dataset were Anesthesia & Analgesia 1133, Anesthesiology 992, and Pain 671. Eight pillar topics were identified and categorized as basic or clinical sciences based on a hierarchical clustering analysis. Amongst the pillar topics, “Cells & Proteomics” had both the highest annual and total number of abstracts. Interestingly, there was an overall upward trend for all topics spanning the years 2000–2022. However, when focusing on the period from 2015 to 2022, topics “Cells & Proteomics” and “Pulmonology” exhibit a downward trajectory. Additionally, various subtopics were identified, with notable increasing trends in “Aneurysms”, “Covid 19 Pandemic”, and “Artificial intelligence & Machine Learning”. Conclusion: Our work offers a comprehensive analysis of the anesthesiology research landscape by providing insights into pillar topics, and trending subtopics. These findings contribute to a better understanding of anesthesiology research and can guide future directions.
KW - Anesthesiology research trends
KW - Artificial intelligence
KW - Machine Learning
KW - NIH-funded research
KW - Pillar topics
KW - Topic modeling
KW - Trending subtopics
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U2 - 10.1016/j.heliyon.2024.e29050
DO - 10.1016/j.heliyon.2024.e29050
M3 - Article
C2 - 38623206
AN - SCOPUS:85189890614
SN - 2405-8440
VL - 10
JO - Heliyon
JF - Heliyon
IS - 7
M1 - e29050
ER -