Thomas Desautels
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
- Family Practice top 5%
- Clinical Reasoning and Diagnostic Skills
Papers in
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- Sepsis Diagnosis and Treatment 3
-
- Metaheuristic Optimization Algorithms Research 1
- Co-authors
- Ritankar Das (6 shared papers)Jacob Calvert (6 shared papers)Melissa Jay (5 shared papers)Jana Hoffman (4 shared papers)Uli K. Chettipally (4 shared papers)David J. Wales (2 shared papers)Yaniv Kerem (2 shared papers)Chris W. Barton (2 shared papers)
- Journals
- Scientific Reports (1 paper)Neural Computing and Applications (1 paper)IEEE Transactions on Biomedical Engineering (1 paper)BMJ Open (1 paper)Journal of Machine Learning Research (1 paper)
- Partner nations
- United StatesUnited KingdomSwitzerland
In The Last Decade
Thomas Desautels
10 papers receiving 626 citations
Thomas Desautels's Hit Papers
Peers
Comparison fields: 5 of 88
- Health Informatics 50
- Family Practice 30
- Health Information Management 66
- Epidemiology 300
- Artificial Intelligence 262
Countries citing papers authored by Thomas Desautels
This map shows the geographic impact of Thomas Desautels'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 Thomas Desautels with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Desautels more than expected).
Fields of papers citing papers by Thomas Desautels
This network shows the impact of papers produced by Thomas Desautels. 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 Thomas Desautels. The network helps show where Thomas Desautels may publish in the future.
Co-authors
The 25 scholars most cited alongside Thomas Desautels, 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 | Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach Hit paper breakdown → | 2016 | 349 |
| 2 | 2017 | 84 | |
| 3 | 2016 | 51 | |
| 4 | 2016 | 42 | |
| 5 | 2017 | 40 | |
| 6 | 2012 | 38 | |
| 7 | 2022 | 16 | |
| 8 | 2016 | 15 | |
| 9 | 2015 | 11 | |
| 10 | 2023 | 1 | |
| 11 | 2025 | 0 |
About Thomas Desautels
Thomas Desautels is a scholar working on Epidemiology, Artificial Intelligence, Molecular Biology, Radiology, Nuclear Medicine and Imaging and Pathology and Forensic Medicine, having authored 11 papers that have together received 647 indexed citations. Recurring topics across this work include Sepsis Diagnosis and Treatment (3 papers), Monoclonal and Polyclonal Antibodies Research (2 papers), Protein purification and stability (1 paper), Vagus Nerve Stimulation Research (1 paper), Spinal Cord Injury Research (1 paper), Bacterial Infections and Vaccines (1 paper), Statistical Methods in Epidemiology (1 paper) and Metaheuristic Optimization Algorithms Research (1 paper). The work is most often cited by research in Health Informatics (50 citations), Family Practice (30 citations), Health Information Management (66 citations), Epidemiology (300 citations) and Artificial Intelligence (262 citations). Thomas Desautels has collaborated with scholars based in United States, United Kingdom and Switzerland. Frequent co-authors include Ritankar Das, Jacob Calvert, Melissa Jay, Jana Hoffman, Uli K. Chettipally, David J. Wales, Yaniv Kerem, Chris W. Barton, Mitchell D. Feldman and David Shimabukuro. Their work appears in journals such as Scientific Reports, Neural Computing and Applications, IEEE Transactions on Biomedical Engineering, BMJ Open and Journal of Machine Learning 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.