Comprehensive overview of the anesthesiology research landscape: A machine Learning Analysis of 737 NIH-funded anesthesiology primary Investigator ' s publication trends

被引：0

作者：

Ghanem, Marc ^{[1
]}

Espinosa, Camilo ^{[1
,2
,3
,4
]}

Chung, Philip ^{[1
]}

Reincke, Momsen ^{[1
]}

Harrison, Natasha ^{[1
,4
]}

Phongpreecha, Thanaphong ^{[1
,4
,5
]}

Shome, Sayane ^{[1
,3
,4
]}

Saarunya, Geetha ^{[1
,3
,4
]}

Berson, Eloise ^{[1
,4
,5
]}

James, Tomin ^{[1
]}

Xie, Feng ^{[1
,3
,4
]}

Shu, Chi-Hung ^{[1
]}

Hazra, Debapriya ^{[1
,3
,4
]}

Mataraso, Samson ^{[1
,3
,4
]}

Kim, Yeasul ^{[1
,3
,4
]}

Seong, David ^{[1
,2
,6
,7
]}

Chakraborty, Dipro ^{[1
,3
,4
]}

Studer, Manuel ^{[1
]}

Xue, Lei ^{[1
,3
,4
]}

Maric, Ivana ^{[3
]}

Chang, Alan L. ^{[1
,3
,4
]}

Tjoa, Erico ^{[1
]}

Gaudilliere, Brice ^{[1
]}

Tawfik, Vivianne L. ^{[1
]}

Mackey, Sean ^{[1
]}

Aghaeepour, Nima ^{[1
,3
,4
]}

机构：

[1] Stanford Univ, Sch Med, Dept Anesthesiol Perioperat & Pain Med, Stanford, CA 94305 USA

[2] Stanford Univ, Sch Med, Immunol Program, Stanford, CA USA

[3] Stanford Univ, Sch Med, Dept Pediat, Stanford, CA USA

[4] Stanford Univ, Dept Biomed Data Sci, Stanford, CA USA

[5] Stanford Univ, Sch Med, Dept Pathol, Stanford, CA USA

[6] Stanford Univ, Sch Med, Med Scientist Training Program, Stanford, CA USA

[7] Stanford Univ, Sch Med, Dept Microbiol & Immunol, Stanford, CA USA

来源：

HELIYON | 2024年 / 10卷 / 07期

关键词：

Anesthesiology research trends; Topic modeling; Pillar topics; Trending subtopics; NIH-funded research; Artificial intelligence; Machine Learning; CHRONIC PAIN; UNITED-STATES;

D O I：

10.1016/j.heliyon.2024.e29050

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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 decisionmaking 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.

引用

页数：12