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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- Trauma, Hemostasis, Coagulopathy, Resuscitation 2
- Co-authors
- Trevor L. Adams (1 shared paper)Bala G. Nair (1 shared paper)Scott Lundberg (1 shared paper)Shu-Fang Newman (1 shared paper)Jerry W. Kim (1 shared paper)Monica Shanta Vavilala (1 shared paper)Su‐In Lee (1 shared paper)Mayumi Horibe (1 shared paper)
- Journals
- Journal of Cardiothoracic and Vascular Anesthesia (4 papers)Seminars in Cardiothoracic and Vascular Anesthesia (2 papers)Anesthesia & Analgesia (2 papers)Anesthesiology Clinics (1 paper)Thrombosis Research (1 paper)
- Partner nations
- United States
In The Last Decade
Michael J. Eisses
17 papers receiving 1.6k citations
Michael J. Eisses's Hit Papers
Peers
Comparison fields: 5 of 173
- Health Informatics 122
- Health Information Management 64
- Artificial Intelligence 317
- Critical Care and Intensive Care Medicine 39
- Developmental Neuroscience 25
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 | 1428 |
| 2 | 2017 | 77 | |
| 3 | 2016 | 34 | |
| 4 | 2004 | 23 | |
| 5 | 2007 | 23 | |
| 6 | 2009 | 19 | |
| 7 | 2006 | 11 | |
| 8 | 2005 | 10 | |
| 9 | 2018 | 8 | |
| 10 | 2017 | 7 | |
| 11 | 2009 | 6 | |
| 12 | 2010 | 6 | |
| 13 | 2015 | 6 | |
| 14 | 2009 | 5 | |
| 15 | 2007 | 4 | |
| 16 | 2019 | 3 | |
| 17 | 2018 | 2 | |
| 18 | 2010 | 0 |
About Michael J. Eisses
Michael J. Eisses is a scholar working on Developmental Neuroscience, Critical Care and Intensive Care Medicine, Anesthesiology and Pain Medicine, Pediatrics, Perinatology and Child Health and Genetics, having authored 18 papers that have together received 1.7k indexed citations. Recurring topics across this work include Congenital Heart Disease Studies (4 papers), Neonatal Health and Biochemistry (3 papers), Tracheal and airway disorders (2 papers), Blood groups and transfusion (2 papers), Hemoglobinopathies and Related Disorders (2 papers), Trauma, Hemostasis, Coagulopathy, Resuscitation (2 papers), Mechanical Circulatory Support Devices (2 papers) and Cardiac, Anesthesia and Surgical Outcomes (2 papers). The work is most often cited by research in Health Informatics (122 citations), Health Information Management (64 citations), Artificial Intelligence (317 citations), Critical Care and Intensive Care Medicine (39 citations) and Developmental Neuroscience (25 citations). Michael J. Eisses has collaborated with scholars based in United States. Frequent co-authors include Trevor L. Adams, Bala G. Nair, Scott Lundberg, Shu-Fang Newman, Jerry W. Kim, Monica Shanta Vavilala, Su‐In Lee, Mayumi Horibe, David E. Liston and Wayne Leslie Chandler. Their work appears in journals such as Journal of Cardiothoracic and Vascular Anesthesia, Seminars in Cardiothoracic and Vascular Anesthesia, Anesthesia & Analgesia, 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.