World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
65
Citations
15438
World Ranking
2475
National Ranking
1241

Hua Xu publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Hua Xu sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 346 publications — 81st percentile

81% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Hua Xu D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Hua Xu sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 65 D-Index — 83rd percentile

83% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Hua Xu is affiliated with Yale University in the United States and has produced a substantial body of research spanning medicine and computer science, with a particular focus on artificial intelligence and health informatics. Their scholarly contributions reflect a multidisciplinary approach that integrates computational methods with biomedical applications.

Their recent research includes the following publications:

  • Immediate psychological distress in quarantined patients with COVID-19 and its association with peripheral inflammation: A mixed-method study, 2020, Brain Behavior and Immunity
  • The All of Us Research Program: Data quality, utility, and diversity, 2022, Patterns
  • Improving large language models for clinical named entity recognition via prompt engineering, 2024, Journal of the American Medical Informatics Association
  • Leveraging Generative AI and Large Language Models: A Comprehensive Roadmap for Healthcare Integration, 2023, Healthcare
  • Distribution and weathering characteristics of microplastics in paddy soils following long-term mulching: A field study in Southwest China, 2022, The Science of The Total Environment

Hua Xu's frequent collaborators include Yujia Zhou, Yong Chen, Jiang Bian, Xiaoqian Jiang, and Hongfang Liu, each having coauthored 17 to 19 publications together. This highlights extensive collaborative work within their research network.

The venues where Hua Xu publishes most frequently are:

  • Journal of the American Medical Informatics Association (25 publications)
  • arXiv (Cornell University) (24 publications)
  • bioRxiv (Cold Spring Harbor Laboratory) (17 publications)
  • Journal of Biomedical Informatics (16 publications)
  • SSRN Electronic Journal (9 publications)

Hua Xu's primary fields of study are medicine with 190 publications and computer science with 177 publications. Within these, specific subfields of focus include:

  • Artificial Intelligence (146 publications)
  • Molecular Biology (93 publications)
  • Electrical and Electronic Engineering (21 publications)
  • Health Informatics (20 publications)
  • Surgery (20 publications)

The main research topics covered by Hua Xu encompass:

  • Biomedical Text Mining and Ontologies (136 publications)
  • Topic Modeling (120 publications)
  • Machine Learning in Healthcare (74 publications)
  • Artificial Intelligence in Healthcare and Education (40 publications)
  • Natural Language Processing Techniques (40 publications)
  • Artificial Intelligence in Healthcare (20 publications)
  • Computational Drug Discovery Methods (18 publications)

In addition to journal articles, Hua Xu has contributed to book publications. One known work is titled Health Information Processing, published by Springer Science+Business Media in 2024, which has received at least one citation.

Best Publications

  • Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data

    Joshua C. Denny;Lisa Bastarache;Marylyn D. Ritchie;Robert J. Carroll

  • MedEx: a medication information extraction system for clinical narratives.

    Hua Xu;Shane P Stenner;Son Doan;Kevin B Johnson

  • Data from clinical notes: a perspective on the tension between structure and flexible documentation.

    S. Trent Rosenbloom;Joshua C. Denny;Hua Xu;Nancy M. Lorenzi

  • Deep learning in clinical natural language processing: a methodical review.

    Stephen Wu;Kirk Roberts;Surabhi Datta;Jingcheng Du

  • The CHEMDNER corpus of chemicals and drugs and its annotation principles.

    Martin Krallinger;Obdulia Rabal;Florian Leitner;Miguel Vazquez

  • CLAMP - a toolkit for efficiently building customized clinical natural language processing pipelines.

    Ergin Soysal;Jingqi Wang;Min Jiang;Yonghui Wu

  • A systematic analysis of FDA-approved anticancer drugs

    Jingchun Sun;Qiang Wei;Yubo Zhou;Jingqi Wang

  • A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries.

    Min Jiang;Yukun Chen;Mei Liu;S Trent Rosenbloom

  • Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs

    Mei Liu;Yonghui Wu;Yukun Chen;Jingchun Sun

  • Enhancing clinical concept extraction with contextual embeddings.

    Yuqi Si;Jingqi Wang;Hua Xu;Kirk E. Roberts

  • Automated Acquisition of Disease–Drug Knowledge from Biomedical and Clinical Documents: An Initial Study

    Elizabeth S. Chen;Elizabeth S. Chen;Elizabeth S. Chen;George Hripcsak;Hua Xu;Marianthi Markatou

  • Portability of an algorithm to identify rheumatoid arthritis in electronic health records

    Robert J. Carroll;William K. Thompson;Anne E. Eyler;Arthur M. Mandelin

  • Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality

    Hua Xu;Melinda C Aldrich;Qingxia Chen;Hongfang Liu

  • The emerging role of electronic medical records in pharmacogenomics

    R. A. Wilke;H. Xu;J. C. Denny;D. M. Roden

  • Entity recognition from clinical texts via recurrent neural network

    Zengjian Liu;Ming Yang;Xiaolong Wang;Qingcai Chen

  • A comprehensive study of named entity recognition in Chinese clinical text

    Jianbo Lei;Buzhou Tang;Xueqin Lu;Kaihua Gao

  • Erratum To: Cisplatin-induced epigenetic activation of miR-34a sensitizes bladder cancer cells to chemotherapy

    Heng Li;Gan Yu;Runlin Shi;Bin Lang

  • Evaluating word representation features in biomedical named entity recognition tasks.

    Buzhou Tang;Hongxin Cao;Xiaolong Wang;Qingcai Chen

  • A study of active learning methods for named entity recognition in clinical text

    Yukun Chen;Thomas A. Lasko;Qiaozhu Mei;Joshua C. Denny

  • Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network.

    Yonghui Wu;Min Jiang;Jianbo Lei;Hua Xu

Frequent Co-Authors

Joshua C. Denny
Joshua C. Denny National Institutes of Health
Buzhou Tang
Buzhou Tang Harbin Institute of Technology
Trevor Cohen
Trevor Cohen University of Washington
Zhongming Zhao
Zhongming Zhao The University of Texas Health Science Center at Houston
Dan M. Roden
Dan M. Roden Vanderbilt University Medical Center
Lucila Ohno-Machado
Lucila Ohno-Machado University of California, San Diego
Hongfang Liu
Hongfang Liu The University of Texas Health Science Center at Houston
Carol Friedman
Carol Friedman Columbia University
Fayez K. Ghishan
Fayez K. Ghishan University of Arizona
Qiaozhu Mei
Qiaozhu Mei University of Michigan–Ann Arbor

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