Jonas Häggström
Impact in
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- Artificial Intelligence in Healthcare and Education
- Statistics and Probability top 10%
- Statistical Methods in Clinical Trials
Papers in
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- Statistical Methods in Clinical Trials 2
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- COVID-19 Clinical Research Studies 1
- Co-authors
- Jay Park (4 shared papers)Edward J. Mills (2 shared papers)Kristian Thorlund (1 shared paper)Louis Dron (3 shared papers)Andrew D. Morris (1 shared paper)Névine Zariffa (1 shared paper)Alind Gupta (2 shared papers)Paul Arora (2 shared papers)
- Journals
- Journal of Medical Internet Research (1 paper)International Journal of Infectious Diseases (1 paper)American Journal of Tropical Medicine and Hygiene (1 paper)The Lancet Digital Health (1 paper)American Journal of Clinical Nutrition (1 paper)
- Partner nations
- United StatesCanadaUnited Kingdom
In The Last Decade
Jonas Häggström
8 papers receiving 225 citations
Peers
Comparison fields: 5 of 79
- Health Informatics 8
- Statistics and Probability 40
- Health Information Management 8
- Radiology, Nuclear Medicine and Imaging 27
- Statistics, Probability and Uncertainty 9
Countries citing papers authored by Jonas Häggström
This map shows the geographic impact of Jonas Häggström'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 Jonas Häggström with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonas Häggström more than expected).
Fields of papers citing papers by Jonas Häggström
This network shows the impact of papers produced by Jonas Häggström. 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 Jonas Häggström. The network helps show where Jonas Häggström may publish in the future.
Co-authors
The 25 scholars most cited alongside Jonas Häggström, 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 | 2018 | 100 | |
| 2 | 2022 | 36 | |
| 3 | 2008 | 33 | |
| 4 | 2020 | 24 | |
| 5 | 2021 | 17 | |
| 6 | 2022 | 9 | |
| 7 | 2023 | 7 | |
| 8 | 2021 | 5 |
About Jonas Häggström
Jonas Häggström is a scholar working on Statistics and Probability, Infectious Diseases, Oncology, Radiology, Nuclear Medicine and Imaging and Statistics, Probability and Uncertainty, having authored 8 papers that have together received 231 indexed citations. Recurring topics across this work include COVID-19 and healthcare impacts (2 papers), Statistical Methods in Clinical Trials (2 papers), Vaccine Coverage and Hesitancy (1 paper), T-cell and B-cell Immunology (1 paper), Probabilistic and Robust Engineering Design (1 paper), Meta-analysis and systematic reviews (1 paper), Artificial Intelligence in Healthcare and Education (1 paper) and COVID-19 Clinical Research Studies (1 paper). The work is most often cited by research in Health Informatics (8 citations), Statistics and Probability (40 citations), Health Information Management (8 citations), Radiology, Nuclear Medicine and Imaging (27 citations) and Statistics, Probability and Uncertainty (9 citations). Jonas Häggström has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Jay Park, Edward J. Mills, Kristian Thorlund, Louis Dron, Andrew D. Morris, Névine Zariffa, Alind Gupta, Paul Arora, Sjúrđur F. Olsen and Sören Möller. Their work appears in journals such as Journal of Medical Internet Research, International Journal of Infectious Diseases, American Journal of Tropical Medicine and Hygiene, The Lancet Digital Health and American Journal of Clinical Nutrition.
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.