Aokun Chen

1.5k total citations · 1 hit paper
24 papers, 743 citations indexed

About

Aokun Chen is a scholar working on Artificial Intelligence, Signal Processing and Computer Networks and Communications. According to data from OpenAlex, Aokun Chen has authored 24 papers receiving a total of 743 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 5 papers in Signal Processing and 3 papers in Computer Networks and Communications. Recurrent topics in Aokun Chen's work include Machine Learning in Healthcare (8 papers), Topic Modeling (7 papers) and Advanced Malware Detection Techniques (5 papers). Aokun Chen is often cited by papers focused on Machine Learning in Healthcare (8 papers), Topic Modeling (7 papers) and Advanced Malware Detection Techniques (5 papers). Aokun Chen collaborates with scholars based in United States, Brazil and Singapore. Aokun Chen's co-authors include Jiang Bian, Yonghui Wu, Xi Yang, Elizabeth Shenkman, Anthony Costa, Mona G. Flores, Tanja Magoč, Ying Zhang, Gloria Lipori and Duane A. Mitchell and has published in prestigious journals such as Circulation, Diabetes and BMC Public Health.

In The Last Decade

Aokun Chen

19 papers receiving 719 citations

Hit Papers

A large language model for electronic health records 2022 2026 2023 2024 2022 100 200 300 400
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.

  1. A large language model for electronic health records
    2022 434 citations Xi Yang, Aokun Chen et al. npj Digital Medicine profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Aokun Chen United States 8 405 288 135 95 90 24 743
Tanja Magoč United States 10 383 0.9× 271 0.9× 132 1.0× 108 1.1× 92 1.0× 33 810
David C. Kale United States 11 467 1.2× 261 0.9× 130 1.0× 179 1.9× 72 0.8× 16 943
Artuur Leeuwenberg Netherlands 8 275 0.7× 123 0.4× 77 0.6× 58 0.6× 61 0.7× 23 579
Khalid Gaffer Mohamed Saudi Arabia 6 164 0.4× 152 0.5× 146 1.1× 100 1.1× 59 0.7× 9 587
Kaleb E Smith United States 3 267 0.7× 190 0.7× 89 0.7× 69 0.7× 69 0.8× 5 471
Emily Alsentzer United States 9 827 2.0× 255 0.9× 134 1.0× 109 1.1× 333 3.7× 22 1.2k
Jan‐Niklas Eckardt Germany 9 331 0.8× 370 1.3× 234 1.7× 54 0.6× 88 1.0× 21 810
HongJu Zhang United States 7 225 0.6× 192 0.7× 208 1.5× 259 2.7× 64 0.7× 7 992
Colin B. Compas United States 7 262 0.6× 182 0.6× 190 1.4× 68 0.7× 75 0.8× 15 618
Yilin Ning Singapore 17 230 0.6× 150 0.5× 104 0.8× 89 0.9× 44 0.5× 58 822

Countries citing papers authored by Aokun Chen

Since Specialization
Citations

This map shows the geographic impact of Aokun Chen'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 Aokun Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aokun Chen more than expected).

Fields of papers citing papers by Aokun Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Aokun Chen. 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 Aokun Chen. The network helps show where Aokun Chen may publish in the future.

Co-authorship network of co-authors of Aokun Chen

This figure shows the co-authorship network connecting the top 25 collaborators of Aokun Chen. A scholar is included among the top collaborators of Aokun Chen 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 Aokun Chen. Aokun Chen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Zhou, Tiancheng, Dong Yang, Kaleb Smith, et al.. (2025). From image to report: automating lung cancer screening interpretation and reporting with vision-language models. Journal of Biomedical Informatics. 171. 104931–104931.
2.
Xie, Qianqian, Qingyu Chen, Aokun Chen, et al.. (2025). Medical foundation large language models for comprehensive text analysis and beyond. npj Digital Medicine. 8(1). 141–141. 16 indexed citations
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Cilar, Leona, Aokun Chen, Maxim Topaz, et al.. (2025). A Brief Review on Benchmarking for Large Language Models Evaluation in Healthcare. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. 15(2). 4 indexed citations
5.
Cheng, Peng, Xi Yang, Aokun Chen, et al.. (2024). Generative large language models are all-purpose text analytics engines: text-to-text learning is all your need. Journal of the American Medical Informatics Association. 31(9). 1892–1903. 10 indexed citations
6.
Cheng, Peng, Xi Yang, Kaleb E Smith, et al.. (2024). Model tuning or prompt Tuning? a study of large language models for clinical concept and relation extraction. Journal of Biomedical Informatics. 153. 104630–104630. 26 indexed citations
7.
Yang, Shuang, Xi Yang, Tianchen Lyu, et al.. (2024). Extracting Pulmonary Nodules and Nodule Characteristics from Radiology Reports of Lung Cancer Screening Patients Using Transformer Models. PubMed. 8(3). 463–477. 5 indexed citations
8.
Chen, Aokun, Yun‐Peng Zhao, Hui Hu, et al.. (2024). Exploring the Relation between Contextual Social Determinants of Health and COVID-19 Occurrence and Hospitalization. Informatics. 11(1). 4–4. 1 indexed citations
9.
Chen, Aokun, John M. Allen, Yi Guo, et al.. (2024). Social determinants of health and newer glucose-lowering drugs adoption among US Medicare beneficiaries with type 2 diabetes. Journal of Managed Care & Specialty Pharmacy. 30(11). 1298–1307. 1 indexed citations
10.
Cheng, Peng, Xi Yang, Aokun Chen, et al.. (2023). A study of generative large language model for medical research and healthcare. npj Digital Medicine. 6(1). 210–210. 186 indexed citations
11.
Chen, Aokun, Zehao Yu, Xi Yang, et al.. (2023). Contextualized medication information extraction using Transformer-based deep learning architectures. Journal of Biomedical Informatics. 142. 104370–104370. 21 indexed citations
12.
Yang, Xi, Aokun Chen, Kaleb E Smith, et al.. (2022). A large language model for electronic health records. npj Digital Medicine. 5(1). 194–194. 434 indexed citations breakdown →
13.
Chen, Aokun, et al.. (2022). Online Binary Models are Promising for Distinguishing Temporally Consistent Computer Usage Profiles. IEEE Transactions on Biometrics Behavior and Identity Science. 4(3). 412–423. 1 indexed citations
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Chen, Aokun, Yongqiu Li, Jennifer Woodard, et al.. (2022). The Impact of Race–Ethnicity and Diagnosis of Alzheimer’s Disease and Related Dementias on Mammography Use. Cancers. 14(19). 4726–4726. 1 indexed citations
17.
Chen, Aokun, Qian Li, Xing He, et al.. (2022). Impacts of Eligibility Criteria on Trial Participants' Age in Alzheimer's Disease Clinical Trials.. PubMed. 2022. 368–376. 2 indexed citations
18.
Yuan, Xiaoyong, Pan He, Qile Zhu, et al.. (2021). Learning Fast and Slow: Propedeutica for Real-Time Malware Detection. IEEE Transactions on Neural Networks and Learning Systems. 33(6). 2518–2529. 7 indexed citations
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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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