Daniel Burns
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
- Health Information Management top 10%
- Artificial Intelligence in Healthcare
Papers in
-
- Machine Learning in Healthcare 4
-
- Sepsis Diagnosis and Treatment 3
- Co-authors
- Apostolos Pilaftsis (2 shared papers)Michael Boniface (6 shared papers)Francis Chmiel (4 shared papers)Zlatko Zlatev (2 shared papers)T. Daniels (2 shared papers)N.M. White (2 shared papers)Christopher Duckworth (3 shared papers)Ali Roghanian (1 shared paper)
- Journals
- Scientific Reports (3 papers)Nuclear Physics B (1 paper)BMJ Open (1 paper)Veterinary Immunology and Immunopathology (1 paper)Frontiers in Immunology (1 paper)
- Partner nations
- United KingdomUnited StatesAustralia
In The Last Decade
Daniel Burns
15 papers receiving 199 citations
Peers
Comparison fields: 5 of 70
- Health Informatics 33
- Health Information Management 20
- Nuclear and High Energy Physics 37
- Astronomy and Astrophysics 44
- Artificial Intelligence 58
Countries citing papers authored by Daniel Burns
This map shows the geographic impact of Daniel Burns'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 Burns with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Burns more than expected).
Fields of papers citing papers by Daniel Burns
This network shows the impact of papers produced by Daniel Burns. 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 Burns. The network helps show where Daniel Burns may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniel Burns, 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 | 59 | |
| 2 | 2016 | 36 | |
| 3 | 2023 | 23 | |
| 4 | 2022 | 18 | |
| 5 | 2021 | 15 | |
| 6 | 2019 | 15 | |
| 7 | 2015 | 12 | |
| 8 | 2022 | 11 | |
| 9 | 2024 | 5 | |
| 10 | 2024 | 2 | |
| 11 | 2023 | 2 | |
| 12 | 2018 | 2 | |
| 13 | 2023 | 1 | |
| 14 | 2025 | 1 | |
| 15 | 2022 | 1 | |
| 16 | Variable Hold Time in Dynamic Random Access Memories | 1991 | 0 |
| 17 | 2024 | 0 | |
| 18 | 2023 | 0 |
About Daniel Burns
Daniel Burns is a scholar working on Artificial Intelligence, Epidemiology, Infectious Diseases, Pulmonary and Respiratory Medicine and Immunology, having authored 18 papers that have together received 203 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (4 papers), COVID-19 Clinical Research Studies (3 papers), Immune Cell Function and Interaction (3 papers), Sepsis Diagnosis and Treatment (3 papers), Access Control and Trust (2 papers), Black Holes and Theoretical Physics (2 papers), Respiratory Support and Mechanisms (2 papers) and Cosmology and Gravitation Theories (2 papers). The work is most often cited by research in Health Informatics (33 citations), Health Information Management (20 citations), Nuclear and High Energy Physics (37 citations), Astronomy and Astrophysics (44 citations) and Artificial Intelligence (58 citations). Daniel Burns has collaborated with scholars based in United Kingdom, United States and Australia. Frequent co-authors include Apostolos Pilaftsis, Michael Boniface, Francis Chmiel, Zlatko Zlatev, T. Daniels, N.M. White, Christopher Duckworth, Ali Roghanian, Ivo Tews and Salah Mansour. Their work appears in journals such as Scientific Reports, Nuclear Physics B, BMJ Open, Veterinary Immunology and Immunopathology and Frontiers in Immunology.
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.