Ivan Lerner
Impact in
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- Cardiac Arrest and Resuscitation
Papers in
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- Topic Modeling 4
- Natural Language Processing Techniques 3
- Machine Learning in Healthcare 3
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- Biomedical Text Mining and Ontologies 5
- Co-authors
- Nicolás Paris (3 shared papers)Anita Burgun (4 shared papers)Antoine Neuraz (7 shared papers)Bastien Rance (6 shared papers)Xavier Tannier (1 shared paper)Xavier Jouven (2 shared papers)Éloi Marijon (2 shared papers)Jean‐Philippe Empana (2 shared papers)
- Journals
- Bioinformatics (1 paper)Arteriosclerosis Thrombosis and Vascular Biology (1 paper)Journal of the American College of Cardiology (1 paper)Intensive Care Medicine (1 paper)Journal of Medical Internet Research (1 paper)
- Partner nations
- FranceUnited StatesNetherlands
In The Last Decade
Ivan Lerner
12 papers receiving 238 citations
Ivan Lerner's Hit Papers
Peers
Comparison fields: 5 of 68
- Health Informatics 8
- Emergency Medicine 46
- Cardiology and Cardiovascular Medicine 60
- Artificial Intelligence 76
- Critical Care and Intensive Care Medicine 12
Countries citing papers authored by Ivan Lerner
This map shows the geographic impact of Ivan Lerner'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 Ivan Lerner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ivan Lerner more than expected).
Fields of papers citing papers by Ivan Lerner
This network shows the impact of papers produced by Ivan Lerner. 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 Ivan Lerner. The network helps show where Ivan Lerner may publish in the future.
Co-authors
The 25 scholars most cited alongside Ivan Lerner, 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 | Incidence of Sudden Cardiac Death in the European Union Hit paper breakdown → | 2022 | 88 |
| 2 | 2020 | 52 | |
| 3 | 2019 | 25 | |
| 4 | 2021 | 20 | |
| 5 | 2018 | 18 | |
| 6 | 2022 | 15 | |
| 7 | 2022 | 10 | |
| 8 | 2022 | 7 | |
| 9 | 2022 | 2 | |
| 10 | 2024 | 1 | |
| 11 | 2024 | 1 | |
| 12 | 2024 | 1 | |
| 13 | 2020 | 0 |
About Ivan Lerner
Ivan Lerner is a scholar working on Artificial Intelligence, Molecular Biology, Cardiology and Cardiovascular Medicine, Infectious Diseases and Neurology, having authored 13 papers that have together received 240 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (5 papers), Topic Modeling (4 papers), Natural Language Processing Techniques (3 papers), Machine Learning in Healthcare (3 papers), Pharmacovigilance and Adverse Drug Reactions (1 paper), Cardiac electrophysiology and arrhythmias (1 paper), Mental Health Research Topics (1 paper) and Lung Cancer Diagnosis and Treatment (1 paper). The work is most often cited by research in Health Informatics (8 citations), Emergency Medicine (46 citations), Cardiology and Cardiovascular Medicine (60 citations), Artificial Intelligence (76 citations) and Critical Care and Intensive Care Medicine (12 citations). Ivan Lerner has collaborated with scholars based in France, United States and Netherlands. Frequent co-authors include Nicolás Paris, Anita Burgun, Antoine Neuraz, Bastien Rance, Xavier Tannier, Xavier Jouven, Éloi Marijon, Jean‐Philippe Empana, Nicolas Garcelon and Marieke T. Blom. Their work appears in journals such as Bioinformatics, Arteriosclerosis Thrombosis and Vascular Biology, Journal of the American College of Cardiology, Intensive Care Medicine and Journal of Medical Internet 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.