Michael Burns

1.6k total citations
43 papers, 1.1k citations indexed

About

Michael Burns is a scholar working on Cardiology and Cardiovascular Medicine, Artificial Intelligence and Surgery. According to data from OpenAlex, Michael Burns has authored 43 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Cardiology and Cardiovascular Medicine, 8 papers in Artificial Intelligence and 7 papers in Surgery. Recurrent topics in Michael Burns's work include Cardiac, Anesthesia and Surgical Outcomes (12 papers), Machine Learning in Healthcare (6 papers) and Artificial Intelligence in Healthcare and Education (6 papers). Michael Burns is often cited by papers focused on Cardiac, Anesthesia and Surgical Outcomes (12 papers), Machine Learning in Healthcare (6 papers) and Artificial Intelligence in Healthcare and Education (6 papers). Michael Burns collaborates with scholars based in United States, South Korea and Germany. Michael Burns's co-authors include A. K. Harding, R. Ramaty, Bongjin Kim, John E. Prescott, T. M. Brown, C. C. Grimes, Sachin Kheterpal, Leif Saager, R. V. E. Lovelace and Michael R. Mathis and has published in prestigious journals such as The Astrophysical Journal, Applied and Environmental Microbiology and Anesthesiology.

In The Last Decade

Michael Burns

38 papers receiving 1.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Michael Burns United States 16 301 222 214 188 101 43 1.1k
Fernando Nobre Brazil 30 343 1.1× 54 0.2× 392 1.8× 91 0.5× 197 2.9k
Фредрик Андерссон Sweden 23 74 0.2× 203 0.9× 13 0.1× 18 0.1× 31 0.3× 146 1.8k
David L. Crawford United States 21 47 0.2× 347 1.6× 43 0.2× 340 1.8× 10 0.1× 73 1.3k
Eric D. Carlson United States 22 132 0.4× 631 2.8× 122 0.6× 69 0.4× 5 0.0× 81 1.7k
Peter G. Nelson United Kingdom 18 276 0.9× 405 1.8× 86 0.4× 108 0.6× 76 1.2k
J. Liu China 12 272 0.9× 141 0.6× 27 0.1× 58 0.3× 4 0.0× 91 876
David W. Meltzer United States 12 120 0.4× 226 1.0× 65 0.3× 76 0.4× 20 1.0k
Ajit Suri United States 20 55 0.2× 204 0.9× 59 0.3× 52 0.3× 54 955
Xiao-Fei Zhang China 29 1.2k 4.1× 43 0.2× 61 0.3× 52 0.3× 8 0.1× 161 2.4k

Countries citing papers authored by Michael Burns

Since Specialization
Citations

This map shows the geographic impact of Michael Burns's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michael Burns with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Burns more than expected).

Fields of papers citing papers by Michael Burns

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Burns. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michael Burns. The network helps show where Michael Burns may publish in the future.

Co-authorship network of co-authors of Michael Burns

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Burns. A scholar is included among the top collaborators of Michael Burns based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michael Burns. Michael Burns is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Lonsdale, Hannah, Michael Burns, Richard H. Epstein, et al.. (2025). Strengthening Discovery and Application of Artificial Intelligence in Anesthesiology: A Report from the Anesthesia Research Council. Anesthesiology. 142(4). 599–610. 2 indexed citations
2.
Lonsdale, Hannah, Michael Burns, Richard H. Epstein, et al.. (2025). Strengthening Discovery and Application of Artificial Intelligence in Anesthesiology: A Report from the Anesthesia Research Council. Anesthesia & Analgesia. 140(4). 920–930.
3.
Marino, Simeone, Ruth Cassidy, Yuxuan Wang, et al.. (2025). Medical data sharing and synthetic clinical data generation – maximizing biomedical resource utilization and minimizing participant re-identification risks. npj Digital Medicine. 8(1). 526–526. 1 indexed citations
4.
Burns, Michael, Jonathan P. Wanderer, Patrick J. McCormick, & Hannah Lonsdale. (2025). Artificial Intelligence in Anesthesiology: Reply. Anesthesiology. 143(6). 1666–1667.
5.
Burns, Michael, et al.. (2025). Generative AI costs in large healthcare systems, an example in revenue cycle. npj Digital Medicine. 8(1). 579–579.
6.
Burns, Michael, Paul E. Hilliard, Graciela Mentz, et al.. (2024). Variation in Intraoperative Opioid Administration by Patient, Clinician, and Hospital Contribution. JAMA Network Open. 7(1). e2351689–e2351689. 6 indexed citations
7.
Fisher, Clark, Allison M. Janda, Yanhong Deng, et al.. (2024). Opioid Dose Variation in Cardiac Surgery: A Multicenter Study of Practice. Anesthesia & Analgesia. 140(5). 1016–1027. 6 indexed citations
8.
Burns, Michael, et al.. (2023). Development and Testing of a Data Capture Device for Use With Clinical Incentive Spirometers: Testing and Usability Study. PubMed. 8. e46653–e46653. 1 indexed citations
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11.
Vázquez, R., et al.. (2021). Re‐evaluating expanding intravenous catheters in medical practice. Health Science Reports. 4(3). e318–e318. 3 indexed citations
12.
Vázquez, R., et al.. (2021). Swellable catheters based on a dynamic expanding inner diameter. Journal of Materials Science Materials in Medicine. 32(5). 51–51. 2 indexed citations
13.
Mathis, Michael R., Milo Engoren, Hyeon Joo, et al.. (2020). Early Detection of Heart Failure With Reduced Ejection Fraction Using Perioperative Data Among Noncardiac Surgical Patients: A Machine-Learning Approach. Anesthesia & Analgesia. 130(5). 1188–1200. 20 indexed citations
14.
Burns, Michael, Michael R. Mathis, Xinyu Tan, et al.. (2020). Classification of Current Procedural Terminology Codes from Electronic Health Record Data Using Machine Learning. Anesthesiology. 132(4). 738–749. 21 indexed citations
15.
Hofer, Ira, Michael Burns, Samir Kendale, & Jonathan P. Wanderer. (2020). Realistically Integrating Machine Learning Into Clinical Practice: A Road Map of Opportunities, Challenges, and a Potential Future. Anesthesia & Analgesia. 130(5). 1115–1118. 50 indexed citations
16.
Mathis, Michael R., Bhiken I. Naik, Robert E. Freundlich, et al.. (2019). Preoperative Risk and the Association between Hypotension and Postoperative Acute Kidney Injury. Anesthesiology. 132(3). 461–475. 150 indexed citations
17.
Burns, Michael, et al.. (2014). Directed Evolution of Brain-Derived Neurotrophic Factor for Improved Folding and Expression in Saccharomyces cerevisiae. Applied and Environmental Microbiology. 80(18). 5732–5742. 25 indexed citations
18.
Burns, Michael, Marek Perkowski, & L. Jóźwiak. (2002). An efficient approach to decomposition of multi-output Boolean functions with large sets of bound variables. 1. 16–23. 19 indexed citations
19.
Burns, Michael, Marek Perkowski, & L. Jóźwiak. (1998). AN EFFICIENT APPROACH TO DECOMPOSITION WITH LARGE SETS OF BOUND VARIABLES OF MULTI-OUTPUT BOOLEAN FUNCTIONS. 2 indexed citations
20.
Bahar, R. Iris, Michael Burns, Gary D. Hachtel, et al.. (1996). Symbolic computation of logic implications for technology-dependent low-power synthesis. 163–168. 13 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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