Tristan Naumann
- Artificial Intelligence top 0.5%
- Molecular Biology top 10%
- Health Informatics top 0.1%
- Health Information Management top 0.2%
- Radiology, Nuclear Medicine and Imaging top 5%
- Co-authors
- Hoifung PoonNaoto Usuyama裕二 池谷Jianfeng GaoRobert TinnHao ChengMatthew B. A. McDermottMichael Lucas
- Topics
- Machine Learning in Healthcare (16 papers)Topic Modeling (13 papers)Biomedical Text Mining and Ontologies (10 papers)
- Journals
- SHILAP Revista de lepidopterologíaNature MethodsScience Translational Medicine
- Partner nations
- United StatesUnited KingdomCanada
In The Last Decade
Tristan Naumann
34 papers receiving 2.9k citations
Hit Papers
Peers
Comparison fields: 5 of 161
- Artificial Intelligence 2.2k
- Molecular Biology 919
- Health Informatics 429
- Health Information Management 372
- Radiology, Nuclear Medicine and Imaging 299
Countries citing papers authored by Tristan Naumann
This map shows the geographic impact of Tristan Naumann'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 Tristan Naumann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tristan Naumann more than expected).
Fields of papers citing papers by Tristan Naumann
This network shows the impact of papers produced by Tristan Naumann. 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 Tristan Naumann. The network helps show where Tristan Naumann may publish in the future.
Co-authorship network of co-authors of Tristan Naumann
This figure shows the co-authorship network connecting the top 25 collaborators of Tristan Naumann. A scholar is included among the top collaborators of Tristan Naumann based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Tristan Naumann. Tristan Naumann is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 4 | |
| 3 | 31 | |
| 4 | 72 | |
| 5 | 4 | |
| 6 | 2 | |
| 7 | 3 | |
| 8 | 3 | |
| 9 | 4 | |
| 10 | 15 | |
| 11 | 12 | |
| 12 | Clinical Collabsheets: 53 Questions to Guide a Clinical Collaboration. | 3 |
| 13 | Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks | 5 |
| 14 | Publicly Available Clinicalbreakdown → | 781 |
| 15 | 52 | |
| 16 | 6 | |
| 17 | 129 | |
| 18 | 52 | |
| 19 | Unfolding physiological state: mortality modelling in intensive care units | 32 |
| 20 | 65 |
About Tristan Naumann
Tristan Naumann is a scholar working on Health Informatics, Health Information Management and Artificial Intelligence, having authored 34 papers that have together received 3.0k indexed citations. Recurring topics across this work include Machine Learning in Healthcare (16 papers), Topic Modeling (13 papers) and Biomedical Text Mining and Ontologies (10 papers). The work is most often cited by research in Health Informatics (429 citations), Health Information Management (372 citations) and Artificial Intelligence (2.2k citations). Tristan Naumann has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Hoifung Poon, Naoto Usuyama, 裕二 池谷, Jianfeng Gao, Robert Tinn, Hao Cheng, Matthew B. A. McDermott, Michael Lucas, Xiaodong Liu and Wei‐Hung Weng. Their work appears in journals such as SHILAP Revista de lepidopterología, Nature Methods and Science Translational 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.