Michael J. Eisses
Impact in
- Health Informatics top 1%
- Artificial Intelligence in Healthcare and Education
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- Artificial Intelligence in Healthcare
Papers in
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- Neonatal Health and Biochemistry 3
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- Congenital Heart Disease Studies 3
- Co-authors
- David E. Liston (1 shared paper)Su‐In Lee (1 shared paper)Jerry W. Kim (1 shared paper)Shu-Fang Newman (1 shared paper)Scott Lundberg (1 shared paper)Monica S. Vavilala (1 shared paper)Mayumi Horibe (1 shared paper)Bala G. Nair (1 shared paper)
- Journals
- Journal of Cardiothoracic and Vascular Anesthesia (3 papers)Seminars in Cardiothoracic and Vascular Anesthesia (2 papers)Nature Biomedical Engineering (1 paper)Anesthesiology Clinics (1 paper)Thrombosis Research (1 paper)
- Partner nations
- United States
In The Last Decade
Michael J. Eisses
13 papers receiving 1.5k citations
Michael J. Eisses's Hit Papers
Peers
Comparison fields: 5 of 171
- Health Informatics 117
- Health Information Management 62
- Critical Care and Intensive Care Medicine 38
- Artificial Intelligence 287
- Cardiology and Cardiovascular Medicine 121
Countries citing papers authored by Michael J. Eisses
This map shows the geographic impact of Michael J. Eisses'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 J. Eisses with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael J. Eisses more than expected).
Fields of papers citing papers by Michael J. Eisses
This network shows the impact of papers produced by Michael J. Eisses. 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 J. Eisses. The network helps show where Michael J. Eisses may publish in the future.
Co-authors
The 25 scholars most cited alongside Michael J. Eisses, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Explainable machine-learning predictions for the prevention of hypoxaemia during surgery Hit paper breakdown → | 2018 | 1320 |
| 2 | 2017 | 73 | |
| 3 | 2016 | 35 | |
| 4 | 2004 | 23 | |
| 5 | 2007 | 21 | |
| 6 | 2009 | 18 | |
| 7 | 2006 | 11 | |
| 8 | 2005 | 10 | |
| 9 | 2018 | 8 | |
| 10 | 2015 | 6 | |
| 11 | 2009 | 6 | |
| 12 | 2019 | 3 | |
| 13 | 2007 | 2 |
About Michael J. Eisses
Michael J. Eisses is a scholar working on Pediatrics, Perinatology and Child Health, Epidemiology, Surgery, Genetics and Critical Care and Intensive Care Medicine, having authored 13 papers that have together received 1.5k indexed citations. Recurring topics across this work include Neonatal Health and Biochemistry (3 papers), Congenital Heart Disease Studies (3 papers), Hemoglobinopathies and Related Disorders (2 papers), Blood groups and transfusion (2 papers), Trauma, Hemostasis, Coagulopathy, Resuscitation (2 papers), Cardiac, Anesthesia and Surgical Outcomes (1 paper), Cardiac and Coronary Surgery Techniques (1 paper) and Machine Learning in Healthcare (1 paper). The work is most often cited by research in Health Informatics (117 citations), Health Information Management (62 citations), Critical Care and Intensive Care Medicine (38 citations), Artificial Intelligence (287 citations) and Cardiology and Cardiovascular Medicine (121 citations). Michael J. Eisses has collaborated with scholars based in United States. Frequent co-authors include David E. Liston, Su‐In Lee, Jerry W. Kim, Shu-Fang Newman, Scott Lundberg, Monica S. Vavilala, Mayumi Horibe, Bala G. Nair, Trevor Adams and Wayne L. Chandler. Their work appears in journals such as Journal of Cardiothoracic and Vascular Anesthesia, Seminars in Cardiothoracic and Vascular Anesthesia, Nature Biomedical Engineering, Anesthesiology Clinics and Thrombosis Research.
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.