Mathieu Ravaut
Impact in
- Health Informatics top 10%
- Artificial Intelligence in Healthcare and Education
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- Artificial Intelligence in Healthcare
Papers in
-
- Topic Modeling 7
- Natural Language Processing Techniques 5
- Advanced Text Analysis Techniques 3
- Advanced Graph Neural Networks 1
- Text and Document Classification Technologies 1
- Co-authors
- Vinyas Harish (3 shared papers)Laura C. Rosella (3 shared papers)Tomi Poutanen (3 shared papers)Kathy Kornas (3 shared papers)Maksims Volkovs (4 shared papers)Tristan Watson (3 shared papers)Gary F. Lewis (1 shared paper)Alanna Weisman (1 shared paper)
- Journals
- BMJ Open (1 paper)npj Digital Medicine (1 paper)JAMA Network Open (1 paper)JMIR Formative Research (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- SingaporeCanadaUnited States
In The Last Decade
Mathieu Ravaut
12 papers receiving 232 citations
Peers
Comparison fields: 5 of 63
- Health Informatics 24
- Health Information Management 43
- Artificial Intelligence 67
- Oceanography 22
- Endocrinology, Diabetes and Metabolism 25
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 | 87 | |
| 2 | 2021 | 60 | |
| 3 | 2017 | 44 | |
| 4 | 2024 | 14 | |
| 5 | 2022 | 11 | |
| 6 | 2022 | 7 | |
| 7 | 2024 | 5 | |
| 8 | 2020 | 3 | |
| 9 | 2022 | 3 | |
| 10 | 2024 | 2 | |
| 11 | 2023 | 2 | |
| 12 | 2024 | 1 | |
| 13 | 2023 | 0 | |
| 14 | 2024 | 0 |
About Mathieu Ravaut
Mathieu Ravaut is a scholar working on Artificial Intelligence, Health Information Management, Oceanography, Ocean Engineering and Computer Vision and Pattern Recognition, having authored 14 papers that have together received 239 indexed citations. Recurring topics across this work include Topic Modeling (7 papers), Natural Language Processing Techniques (5 papers), Advanced Text Analysis Techniques (3 papers), Advanced Graph Neural Networks (1 paper), Text and Document Classification Technologies (1 paper), Underwater Vehicles and Communication Systems (1 paper), Chronic Disease Management Strategies (1 paper) and Underwater Acoustics Research (1 paper). The work is most often cited by research in Health Informatics (24 citations), Health Information Management (43 citations), Artificial Intelligence (67 citations), Oceanography (22 citations) and Endocrinology, Diabetes and Metabolism (25 citations). Mathieu Ravaut has collaborated with scholars based in Singapore, Canada and United States. Frequent co-authors include Vinyas Harish, Laura C. Rosella, Tomi Poutanen, Kathy Kornas, Maksims Volkovs, Tristan Watson, Gary F. Lewis, Alanna Weisman, Nancy F. Chen and Shafiq Joty. Their work appears in journals such as BMJ Open, npj Digital Medicine, JAMA Network Open, JMIR Formative Research and arXiv (Cornell University).
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.