Hunter Lang
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
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- Topic Modeling
- Machine Learning in Healthcare
- Natural Language Processing Techniques
- Advanced Text Analysis Techniques
Papers in
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- Natural Language Processing Techniques 2
- Machine Learning and Data Classification 1
- Topic Modeling 1
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- Digital Rights Management and Security 1
- Library Science and Information Systems 1
- Co-authors
- David A. Sontag (3 shared papers)Yoon Cheol Kim (2 shared papers)Stefan Hegselmann (1 shared paper)Monica Agrawal (1 shared paper)Hoifung Poon (1 shared paper)Bailin Wang (1 shared paper)Zejiang Shen (1 shared paper)Tomas Pfister (1 shared paper)
- Journals
- arXiv (Cornell University) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- United StatesUnited KingdomGermany
In The Last Decade
Hunter Lang
4 papers receiving 191 citations
Hunter Lang's Hit Papers
Peers
Comparison fields: 5 of 56
- Health Informatics 29
- Artificial Intelligence 113
- Family Practice 4
- Health Information Management 10
- Information Systems 20
Countries citing papers authored by Hunter Lang
This map shows the geographic impact of Hunter Lang'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 Hunter Lang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hunter Lang more than expected).
Fields of papers citing papers by Hunter Lang
This network shows the impact of papers produced by Hunter Lang. 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 Hunter Lang. The network helps show where Hunter Lang may publish in the future.
Co-authors
The 16 scholars most cited alongside Hunter Lang, 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 | Large language models are few-shot clinical information extractors Hit paper breakdown → | 2022 | 190 |
| 2 | 2024 | 7 | |
| 3 | 2021 | 4 | |
| 4 | 2026 | 1 | |
| 5 | 2024 | 0 |
About Hunter Lang
Hunter Lang is a scholar working on Artificial Intelligence, Information Systems, Molecular Biology, Insect Science and Renewable Energy, Sustainability and the Environment, having authored 5 papers that have together received 202 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (2 papers), Machine Learning and Data Classification (1 paper), Topic Modeling (1 paper), Digital Rights Management and Security (1 paper), Biomedical Text Mining and Ontologies (1 paper) and Library Science and Information Systems (1 paper). The work is most often cited by research in Health Informatics (29 citations), Artificial Intelligence (113 citations), Family Practice (4 citations), Health Information Management (10 citations) and Information Systems (20 citations). Hunter Lang has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include David A. Sontag, Yoon Cheol Kim, Stefan Hegselmann, Monica Agrawal, Hoifung Poon, Bailin Wang, Zejiang Shen, Tomas Pfister, Yulia Tsvetkov and Aravindan Vijayaraghavan. Their work appears in journals such as arXiv (Cornell University) and Proceedings of the AAAI Conference on Artificial Intelligence.
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.