Raphaël Couronné
Impact in
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- Artificial Intelligence in Healthcare
Papers in
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- Machine Learning in Healthcare 3
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- Generative Adversarial Networks and Image Synthesis 2
- Co-authors
- Philipp Probst (1 shared paper)Anne‐Laure Boulesteix (1 shared paper)Stanley Durrleman (6 shared papers)Maxime Louis (2 shared papers)Igor Koval (2 shared papers)Jean‐Christophe Corvol (3 shared papers)Johann Faouzi (1 shared paper)Manon Ansart (1 shared paper)
- Journals
- British Journal of Clinical Pharmacology (1 paper)BMC Bioinformatics (1 paper)Medical Image Analysis (1 paper)Annals of Neurology (1 paper)Movement Disorders (1 paper)
- Partner nations
- FranceGermanyUnited Kingdom
In The Last Decade
Raphaël Couronné
12 papers receiving 732 citations
Raphaël Couronné's Hit Papers
Peers
Comparison fields: 5 of 170
- Health Information Management 32
- Health Informatics 8
- Psychiatry and Mental health 56
- Artificial Intelligence 142
- Statistics and Probability 23
Countries citing papers authored by Raphaël Couronné
This map shows the geographic impact of Raphaël Couronné'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 Raphaël Couronné with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Raphaël Couronné more than expected).
Fields of papers citing papers by Raphaël Couronné
This network shows the impact of papers produced by Raphaël Couronné. 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 Raphaël Couronné. The network helps show where Raphaël Couronné may publish in the future.
Co-authors
The 25 scholars most cited alongside Raphaël Couronné, 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 | Random forest versus logistic regression: a large-scale benchmark experiment Hit paper breakdown → | 2018 | 572 |
| 2 | 2020 | 66 | |
| 3 | Blood pressure tracking capabilities of pulse transit times in different arterial segments: A clinical evaluation | 2009 | 32 |
| 4 | 2019 | 17 | |
| 5 | 2023 | 16 | |
| 6 | 2021 | 16 | |
| 7 | 2023 | 11 | |
| 8 | 2019 | 11 | |
| 9 | 2009 | 4 | |
| 10 | 2024 | 3 | |
| 11 | 2017 | 1 | |
| 12 | 2009 | 1 |
About Raphaël Couronné
Raphaël Couronné is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Surgery, Neurology and Cardiology and Cardiovascular Medicine, having authored 12 papers that have together received 750 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (3 papers), Hemodynamic Monitoring and Therapy (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Parkinson's Disease Mechanisms and Treatments (2 papers), Non-Invasive Vital Sign Monitoring (2 papers), Cardiovascular and exercise physiology (1 paper), Epigenetics and DNA Methylation (1 paper) and Cardiovascular Health and Disease Prevention (1 paper). The work is most often cited by research in Health Information Management (32 citations), Health Informatics (8 citations), Psychiatry and Mental health (56 citations), Artificial Intelligence (142 citations) and Statistics and Probability (23 citations). Raphaël Couronné has collaborated with scholars based in France, Germany and United Kingdom. Frequent co-authors include Philipp Probst, Anne‐Laure Boulesteix, Stanley Durrleman, Maxime Louis, Igor Koval, Jean‐Christophe Corvol, Johann Faouzi, Manon Ansart, Olivier Colliot and Adam Wild. Their work appears in journals such as British Journal of Clinical Pharmacology, BMC Bioinformatics, Medical Image Analysis, Annals of Neurology and Movement Disorders.
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.