Tristan Naumann

8.4k total citations · 2 hit papers
34 papers, 3.0k citations indexed

About

Tristan Naumann is a scholar working on Artificial Intelligence, Molecular Biology and Health Informatics. According to data from OpenAlex, Tristan Naumann has authored 34 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Artificial Intelligence, 11 papers in Molecular Biology and 7 papers in Health Informatics. Recurrent topics in Tristan Naumann's work include Machine Learning in Healthcare (16 papers), Topic Modeling (13 papers) and Biomedical Text Mining and Ontologies (10 papers). Tristan Naumann is often cited by papers focused on Machine Learning in Healthcare (16 papers), Topic Modeling (13 papers) and Biomedical Text Mining and Ontologies (10 papers). Tristan Naumann collaborates with scholars based in United States, United Kingdom and Canada. Tristan Naumann's 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 Emily Alsentzer and has published in prestigious journals such as SHILAP Revista de lepidopterología, Nature Methods and Science Translational Medicine.

In The Last Decade

Tristan Naumann

34 papers receiving 2.9k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

2021 1.0k citations 裕二池谷, Robert Tinn et al. profile →
Publicly Available Clinical

2019 781 citations Emily Alsentzer, John R. Murphy et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Tristan Naumann United States	17	2.2k	919	429	372	299	34	3.0k
Sijia Liu United States	24	1.8k 0.8×	904 1.0×	214 0.5×	351 0.9×	257 0.9×	159	3.2k
Jianying Hu United States	25	1.0k 0.5×	590 0.6×	338 0.8×	490 1.3×	234 0.8×	77	2.6k
Kirk Roberts United States	29	1.9k 0.9×	1.0k 1.1×	180 0.4×	250 0.7×	280 0.9×	136	3.0k
Donghyeon Kim South Korea	8	3.0k 1.4×	1.7k 1.9×	280 0.7×	166 0.4×	235 0.8×	24	4.0k
Wonjin Yoon South Korea	6	2.9k 1.3×	1.6k 1.7×	279 0.7×	157 0.4×	235 0.8×	13	3.8k
Sunghwan Sohn United States	33	2.8k 1.3×	2.0k 2.2×	529 1.2×	723 1.9×	417 1.4×	141	4.8k
Edward Choi United States	16	1.8k 0.8×	330 0.4×	243 0.6×	713 1.9×	273 0.9×	48	2.5k
Shyam Visweswaran United States	23	721 0.3×	499 0.5×	226 0.5×	243 0.7×	200 0.7×	129	1.9k
Finale Doshi‐Velez United States	25	1.9k 0.9×	247 0.3×	667 1.6×	244 0.7×	329 1.1×	102	3.4k
Riccardo Miotto United States	18	2.2k 1.0×	753 0.8×	674 1.6×	1.0k 2.8×	839 2.8×	48	4.7k

Countries citing papers authored by Tristan Naumann

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Wong, Cliff, Zelalem Gero, Jaspreet Bagga, et al.. (2025). TRIALSCOPE — A Framework for Clinical Trial Simulation from Real-World Data. NEJM AI. 2(10). 2 indexed citations

Zhang, Sheng, Pengfei Liu, Zelalem Gero, et al.. (2024). DocLens: Multi-aspect Fine-grained Medical Text Evaluation. 649–679. 4 indexed citations

Tinn, Robert, Hao Cheng, 裕二池谷, et al.. (2023). Fine-tuning large neural language models for biomedical natural language processing. Patterns. 4(4). 100729–100729. 72 indexed citations

Poon, Hoifung, et al.. (2023). Precision Health in the Age of Large Language Models. 5825–5826. 4 indexed citations

Zhou, Wenxuan, Sheng Zhang, Tristan Naumann, Muhao Chen, & Hoifung Poon. (2023). Continual Contrastive Finetuning Improves Low-Resource Relation Extraction. 13249–13263. 3 indexed citations

Liu, Fangyu, Qianchu Liu, Shruthi Bannur, et al.. (2023). Compositional Zero-Shot Domain Transfer with Text-to-Text Models. Transactions of the Association for Computational Linguistics. 11. 1097–1113. 2 indexed citations

Mu, Wei, Rajesh C. Rao, Robert Tinn, et al.. (2023). Toward structuring real-world data: Deep learning for extracting oncology information from clinical text with patient-level supervision. Patterns. 4(4). 100726–100726. 15 indexed citations

Lu, Keming, Peter Potash, Xihui Lin, et al.. (2023). Prompt Discriminative Language Models for Domain Adaptation. 247–258. 4 indexed citations

Nguyen, Bichlien H., Jake A. Smith, Yingce Xia, et al.. (2023). What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization. PubMed. 2023. 10520–10542. 3 indexed citations

10.

Zhang, Sheng, Cliff Wong, Naoto Usuyama, et al.. (2021). Modular Self-Supervision for Document-Level Relation Extraction. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 5291–5302. 5 indexed citations

11.

Boag, William, et al.. (2020). Clinical Collabsheets: 53 Questions to Guide a Clinical Collaboration.. 783–812. 3 indexed citations

12.

Nestor, Bret, Matthew B. A. McDermott, Willie Boag, et al.. (2019). Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks. 381–405. 5 indexed citations

13.

Alsentzer, Emily, John R. Murphy, William Boag, et al.. (2019). Publicly Available Clinical. 72–78. 781 indexed citations breakdown →

14.

Ghassemi, Marzyeh, Tristan Naumann, Peter Schulam, et al.. (2019). Practical guidance on artificial intelligence for health-care data. The Lancet Digital Health. 1(4). e157–e159. 52 indexed citations

15.

Zeng, Zexian, Yu Deng, Xiaoyu Li, Tristan Naumann, & Yuan Luo. (2018). Natural Language Processing for EHR-Based Computational Phenotyping. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 16(1). 139–153. 129 indexed citations

16.

Rumshisky, Anna, Marzyeh Ghassemi, Tristan Naumann, et al.. (2016). Predicting early psychiatric readmission with natural language processing of narrative discharge summaries. Translational Psychiatry. 6(10). e921–e921. 129 indexed citations

17.

Celi, Leo Anthony, Sharukh Lokhandwala, Robert A. Montgomery, et al.. (2016). Datathons and Software to Promote Reproducible Research. Journal of Medical Internet Research. 18(8). e230–e230. 6 indexed citations

18.

Aboab, Jérôme, Leo Anthony Celi, Peter Charlton, et al.. (2016). A “datathon” model to support cross-disciplinary collaboration. Science Translational Medicine. 8(333). 333ps8–333ps8. 52 indexed citations

19.

Ghassemi, Marzyeh, Finale Doshi‐Velez, Rohit Joshi, et al.. (2014). Unfolding physiological state: mortality modelling in intensive care units. DSpace@MIT (Massachusetts Institute of Technology). 32 indexed citations

20.

Badawi, Omar, Thomas P. Brennan, Leo Anthony Celi, et al.. (2014). Making Big Data Useful for Health Care: A Summary of the Inaugural MIT Critical Data Conference. JMIR Medical Informatics. 2(2). e22–e22. 65 indexed citations

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.

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