Daniel M. Bean
Impact in
- Health Informatics top 5%
- Infectious Diseases top 10%
- COVID-19 Clinical Research Studies
- SARS-CoV-2 and COVID-19 Research
Papers in
-
- COVID-19 Clinical Research Studies 4
-
- Biomedical Text Mining and Ontologies 6
- Bioinformatics and Genomic Networks 5
- Co-authors
- Richard Dobson (26 shared papers)James Teo (14 shared papers)Rebecca Bendayan (12 shared papers)Stephen G. Oliver (4 shared papers)Željko Kraljević (11 shared papers)Anthony Shek (7 shared papers)Ajay M. Shah (6 shared papers)Kevin O’Gallagher (5 shared papers)
- Journals
- PLoS ONE (4 papers)BMC Cardiovascular Disorders (2 papers)Scientific Reports (2 papers)BMC Medicine (2 papers)Artificial Intelligence in Medicine (1 paper)
- Partner nations
- United KingdomUnited StatesItaly
In The Last Decade
Daniel M. Bean
35 papers receiving 1.0k citations
Peers
Comparison fields: 5 of 109
- Health Informatics 28
- Infectious Diseases 305
- Neurology 170
- Toxicology 27
- Aging 11
Countries citing papers authored by Daniel M. Bean
This map shows the geographic impact of Daniel M. Bean'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 Daniel M. Bean with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel M. Bean more than expected).
Fields of papers citing papers by Daniel M. Bean
This network shows the impact of papers produced by Daniel M. Bean. 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 Daniel M. Bean. The network helps show where Daniel M. Bean may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniel M. Bean, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 35 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 170 | |
| 2 | 2020 | 137 | |
| 3 | 2021 | 99 | |
| 4 | 2017 | 95 | |
| 5 | 2017 | 94 | |
| 6 | 2024 | 58 | |
| 7 | 2014 | 50 | |
| 8 | 2020 | 42 | |
| 9 | 2021 | 25 | |
| 10 | 2016 | 24 | |
| 11 | 2023 | 24 | |
| 12 | 2021 | 22 | |
| 13 | 2020 | 22 | |
| 14 | 2013 | 19 | |
| 15 | 2019 | 18 | |
| 16 | 2017 | 16 | |
| 17 | 2022 | 16 | |
| 18 | 2017 | 16 | |
| 19 | 2019 | 16 | |
| 20 | 2022 | 15 |
About Daniel M. Bean
Daniel M. Bean is a scholar working on Infectious Diseases, Molecular Biology, Artificial Intelligence, Cardiology and Cardiovascular Medicine and Health Information Management, having authored 35 papers that have together received 1.1k indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (6 papers), Bioinformatics and Genomic Networks (5 papers), COVID-19 Clinical Research Studies (4 papers), Topic Modeling (4 papers), Atrial Fibrillation Management and Outcomes (3 papers), Machine Learning in Healthcare (3 papers), Computational Drug Discovery Methods (2 papers) and Healthcare Policy and Management (2 papers). The work is most often cited by research in Health Informatics (28 citations), Infectious Diseases (305 citations), Neurology (170 citations), Toxicology (27 citations) and Aging (11 citations). Daniel M. Bean has collaborated with scholars based in United Kingdom, United States and Italy. Frequent co-authors include Richard Dobson, James Teo, Rebecca Bendayan, Stephen G. Oliver, Željko Kraljević, Anthony Shek, Ajay M. Shah, Kevin O’Gallagher, Rosita Zakeri and Giorgio Favrin. Their work appears in journals such as PLoS ONE, BMC Cardiovascular Disorders, Scientific Reports, BMC Medicine and Artificial Intelligence in Medicine.
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.