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 6
- Natural Language Processing Techniques 4
- Advanced Text Analysis Techniques 2
- Text Readability and Simplification 1
- Advanced Graph Neural Networks 1
- Co-authors
- Maksims Volkovs (4 shared papers)Kathy Kornas (3 shared papers)Tomi Poutanen (3 shared papers)Tristan Watson (3 shared papers)Laura C. Rosella (3 shared papers)Vinyas Harish (3 shared papers)Gary F. Lewis (1 shared paper)Alanna Weisman (1 shared paper)
- Journals
- npj Digital Medicine (1 paper)BMJ Open (1 paper)JAMA Network Open (1 paper)JMIR Formative Research (1 paper)JMIR AI (1 paper)
- Partner nations
- SingaporeCanadaUnited States
In The Last Decade
Mathieu Ravaut
11 papers receiving 194 citations
Peers
Comparison fields: 5 of 57
- Health Informatics 22
- Health Information Management 39
- Oceanography 21
- Artificial Intelligence 50
- Endocrinology, Diabetes and Metabolism 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 | 73 | |
| 2 | 2021 | 54 | |
| 3 | 2017 | 40 | |
| 4 | 2024 | 11 | |
| 5 | 2022 | 9 | |
| 6 | 2022 | 5 | |
| 7 | 2022 | 2 | |
| 8 | 2023 | 2 | |
| 9 | 2020 | 2 | |
| 10 | 2024 | 1 | |
| 11 | 2024 | 1 | |
| 12 | 2024 | 0 | |
| 13 | 2023 | 0 |
About Mathieu Ravaut
Mathieu Ravaut is a scholar working on Artificial Intelligence, Health Information Management, Information Systems, Oceanography and Computer Vision and Pattern Recognition, having authored 13 papers that have together received 200 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Natural Language Processing Techniques (4 papers), Advanced Text Analysis Techniques (2 papers), Chronic Disease Management Strategies (1 paper), Underwater Acoustics Research (1 paper), Text Readability and Simplification (1 paper), Advanced Graph Neural Networks (1 paper) and Geophysical Methods and Applications (1 paper). The work is most often cited by research in Health Informatics (22 citations), Health Information Management (39 citations), Oceanography (21 citations), Artificial Intelligence (50 citations) and Endocrinology, Diabetes and Metabolism (20 citations). Mathieu Ravaut has collaborated with scholars based in Singapore, Canada and United States. Frequent co-authors include Maksims Volkovs, Kathy Kornas, Tomi Poutanen, Tristan Watson, Laura C. Rosella, Vinyas Harish, Gary F. Lewis, Alanna Weisman, Nancy F. Chen and Shafiq Joty. Their work appears in journals such as npj Digital Medicine, BMJ Open, JAMA Network Open, JMIR Formative Research and JMIR AI.
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.