Robert Tinn
Impact in
- Health Informatics top 1%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 2%
- Topic Modeling
- Natural Language Processing Techniques
- Machine Learning in Healthcare
- Advanced Text Analysis Techniques
Papers in
-
- Topic Modeling 3
- Natural Language Processing Techniques 2
- Machine Learning in Healthcare 2
-
- Biomedical Text Mining and Ontologies 3
- Co-authors
- Naoto Usuyama (4 shared papers)Hoifung Poon (5 shared papers)裕二 池谷 (3 shared papers)Tristan Naumann (3 shared papers)Jianfeng Gao (2 shared papers)Hao Cheng (2 shared papers)Michael Lucas (2 shared papers)Xiaodong Liu (1 shared paper)
- Journals
- Patterns (2 papers)Journal of Clinical Oncology (1 paper)ACM Transactions on Computing for Healthcare (1 paper)
- Partner nations
- United States
In The Last Decade
Robert Tinn
5 papers receiving 1.3k citations
Robert Tinn's Hit Papers
Peers
Comparison fields: 5 of 113
- Health Informatics 143
- Artificial Intelligence 813
- Health Information Management 39
- Molecular Biology 434
- Radiology, Nuclear Medicine and Imaging 88
Countries citing papers authored by Robert Tinn
This map shows the geographic impact of Robert Tinn'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 Robert Tinn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert Tinn more than expected).
Fields of papers citing papers by Robert Tinn
This network shows the impact of papers produced by Robert Tinn. 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 Robert Tinn. The network helps show where Robert Tinn may publish in the future.
Co-authors
The 25 scholars most cited alongside Robert Tinn, 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 | Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing Hit paper breakdown → | 2021 | 1196 |
| 2 | 2023 | 80 | |
| 3 | 2023 | 17 | |
| 4 | 2023 | 14 | |
| 5 | 2023 | 1 |
About Robert Tinn
Robert Tinn is a scholar working on Artificial Intelligence, Molecular Biology, Health Informatics, Public Health, Environmental and Occupational Health and Radiology, Nuclear Medicine and Imaging, having authored 5 papers that have together received 1.3k indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (3 papers), Topic Modeling (3 papers), Natural Language Processing Techniques (2 papers), Machine Learning in Healthcare (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper), Artificial Intelligence in Healthcare and Education (1 paper), Cancer Genomics and Diagnostics (1 paper) and Ethics in Clinical Research (1 paper). The work is most often cited by research in Health Informatics (143 citations), Artificial Intelligence (813 citations), Health Information Management (39 citations), Molecular Biology (434 citations) and Radiology, Nuclear Medicine and Imaging (88 citations). Robert Tinn has collaborated with scholars based in United States. Frequent co-authors include Naoto Usuyama, Hoifung Poon, 裕二 池谷, Tristan Naumann, Jianfeng Gao, Hao Cheng, Michael Lucas, Xiaodong Liu, Xiaodong Liu and Javier Alvarez-Valle. Their work appears in journals such as Patterns, Journal of Clinical Oncology and ACM Transactions on Computing for Healthcare.
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.