Mathieu Ravaut
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
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- Artificial Intelligence in Healthcare
Papers in
-
- Topic Modeling 7
- Natural Language Processing Techniques 4
- Advanced Text Analysis Techniques 2
- Machine Learning in Healthcare 2
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- Chronic Disease Management Strategies 3
- Co-authors
- Kathy Kornas (3 shared papers)Vinyas Harish (3 shared papers)Laura C. Rosella (3 shared papers)Maksims Volkovs (4 shared papers)Tomi Poutanen (3 shared papers)Tristan Watson (3 shared papers)Gary F. Lewis (1 shared paper)Alanna Weisman (1 shared paper)
- Journals
- JAMA Network Open (1 paper)BMJ Open (1 paper)npj Digital Medicine (1 paper)JMIR Formative Research (1 paper)PubMed (1 paper)
- Partner nations
- SingaporeCanadaUnited States
In The Last Decade
Mathieu Ravaut
11 papers receiving 182 citations
Peers
Comparison fields: 5 of 71
- Health Informatics 24
- Health Information Management 55
- Endocrinology, Diabetes and Metabolism 33
- Artificial Intelligence 59
- Oceanography 20
Countries citing papers authored by Mathieu Ravaut
This map shows the geographic impact of Mathieu Ravaut'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 Mathieu Ravaut with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mathieu Ravaut more than expected).
Fields of papers citing papers by Mathieu Ravaut
This network shows the impact of papers produced by Mathieu Ravaut. 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 Mathieu Ravaut. The network helps show where Mathieu Ravaut may publish in the future.
Co-authors
The 25 scholars most cited alongside Mathieu Ravaut, 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 | 2021 | 71 | |
| 2 | 2021 | 50 | |
| 3 | 2017 | 39 | |
| 4 | 2024 | 9 | |
| 5 | 2022 | 8 | |
| 6 | 2022 | 5 | |
| 7 | 2022 | 1 | |
| 8 | 2024 | 1 | |
| 9 | 2024 | 1 | |
| 10 | 2023 | 1 | |
| 11 | 2020 | 1 | |
| 12 | 2024 | 0 | |
| 13 | 2023 | 0 |
About Mathieu Ravaut
Mathieu Ravaut is a scholar working on Artificial Intelligence, Epidemiology, General Health Professions, Health Information Management and Information Systems, having authored 13 papers that have together received 187 indexed citations. Recurring topics across this work include Topic Modeling (7 papers), Natural Language Processing Techniques (4 papers), Chronic Disease Management Strategies (3 papers), Advanced Text Analysis Techniques (2 papers), Machine Learning in Healthcare (2 papers), Diabetes, Cardiovascular Risks, and Lipoproteins (1 paper), Medical Coding and Health Information (1 paper) and COVID-19 diagnosis using AI (1 paper). The work is most often cited by research in Health Informatics (24 citations), Health Information Management (55 citations), Endocrinology, Diabetes and Metabolism (33 citations), Artificial Intelligence (59 citations) and Oceanography (20 citations). Mathieu Ravaut has collaborated with scholars based in Singapore, Canada and United States. Frequent co-authors include Kathy Kornas, Vinyas Harish, Laura C. Rosella, Maksims Volkovs, Tomi Poutanen, Tristan Watson, Gary F. Lewis, Alanna Weisman, Nancy F. Chen and Shafiq Joty. Their work appears in journals such as JAMA Network Open, BMJ Open, npj Digital Medicine, JMIR Formative Research and PubMed.
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.