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

Marc Ghanem; Camilo Espinosa; Philip Chung; Momsen Reincke; Natasha Harrison; Thanaphong Phongpreecha; Sayane Shome; Geetha Saarunya; Eloise Berson; Tomin James; Feng Xie; Chi-Hung Shu; Debapriya Hazra; Samson Mataraso; Yeasul Kim; David Seong; Dipro Chakraborty; Manuel Studer; Lei Xue; Ivana Marić; Alan L Chang; Erico Tjoa; Brice Gaudillière; Vivianne L Tawfik; Sean Mackey; Nima Aghaeepour

doi:10.1016/j.heliyon.2024.e29050

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

Heliyon. 2024 Apr 3;10(7):e29050. doi: 10.1016/j.heliyon.2024.e29050. eCollection 2024 Apr 15.

Authors

Marc Ghanem¹, Camilo Espinosa^{1

2

3

4}, Philip Chung¹, Momsen Reincke¹, Natasha Harrison^{1

3

4}, Thanaphong Phongpreecha^{1

4

5}, Sayane Shome^{1

3

4}, Geetha Saarunya^{1

3

4}, Eloise Berson^{1

4

5}, Tomin James¹, Feng Xie^{1

3

4}, Chi-Hung Shu¹, Debapriya Hazra^{1

3

4}, Samson Mataraso^{1

3

4}, Yeasul Kim^{1

3

4}, David Seong^{1

2

6

7}, Dipro Chakraborty^{1

3

4}, Manuel Studer¹, Lei Xue^{1

3

4}, Ivana Marić³, Alan L Chang^{1

3

4}, Erico Tjoa¹, Brice Gaudillière¹, Vivianne L Tawfik¹, Sean Mackey¹, Nima Aghaeepour^{1

3

4}

Affiliations

¹ Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA.
² Immunology Program, Stanford University School of Medicine, Stanford, CA, USA.
³ Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
⁴ Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
⁵ Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
⁶ Medical Scientist Training Program, Stanford University School of Medicine, Stanford, CA, USA.
⁷ Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA.

Abstract

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.

Keywords: Anesthesiology research trends; Artificial intelligence; Machine Learning; NIH-funded research; Pillar topics; Topic modeling; Trending subtopics.

Grants and funding

R35 GM138353/GM/NIGMS NIH HHS/United States