World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
4522
World Ranking
12235
National Ranking
4962

Yanshan Wang 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 Yanshan Wang 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 173 publications — 36th percentile

36% of scientists in this discipline score the same or lower.

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

Yanshan Wang 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 Yanshan Wang sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 34 D-Index — 16th percentile

16% of scientists in this discipline score the same or lower.

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

Best Publications

  • Clinical information extraction applications: A literature review.

    Yanshan Wang;Liwei Wang;Majid Rastegar-Mojarad;Sungrim Moon

  • A clinical text classification paradigm using weak supervision and deep representation.

    Yanshan Wang;Sunghwan Sohn;Sijia Liu;Feichen Shen

  • A comparison of word embeddings for the biomedical natural language processing

    Yanshan Wang;Sijia Liu;Naveed Afzal;Majid Rastegar-Mojarad

  • Clinical concept extraction: A methodology review

    Sunyang Fu;Sunyang Fu;David Chen;Huan He;Sijia Liu

  • Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI

    Unknown

  • An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing: Algorithm Development and Validation Study

    Unknown

  • MedSTS: A resource for clinical semantic textual similarity

    Yanshan Wang;Naveed Afzal;Sunyang Fu;Liwei Wang

  • Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records.

    Yanshan Wang;Yiqing Zhao;Terry M. Therneau;Elizabeth J. Atkinson

  • Toward a Learning Health-care System – Knowledge Delivery at the Point of Care Empowered by Big Data and NLP

    Vinod C. Kaggal;Ravikumar Komandur Elayavilli;Saeed Mehrabi;Joshua J. Pankratz

  • Using weak supervision and deep learning to classify clinical notes for identification of current suicidal ideation.

    Marika Cusick;Prakash Adekkanattu;Thomas R. Campion;Evan T. Sholle

  • Clinical documentation variations and NLP system portability: a case study in asthma birth cohorts across institutions.

    Sunghwan Sohn;Yanshan Wang;Chung Il Wi;Elizabeth A. Krusemark

  • Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine.

    Rezarta Islamaj Dogan;Sun Kim;Andrew Chatr-Aryamontri;Chih-Hsuan Wei

  • Use of Natural Language Processing Algorithms to Identify Common Data Elements in Operative Notes for Total Hip Arthroplasty

    Cody C. Wyles;Meagan E. Tibbo;Sunyang Fu;Yanshan Wang

  • The 2019 n2c2/OHNLP Track on Clinical Semantic Textual Similarity: Overview

    Yanshan Wang;Sunyang Fu;Feichen Shen;Sam Henry

  • Natural Language Processing for the Identification of Silent Brain Infarcts From Neuroimaging Reports.

    Sunyang Fu;Lester Y. Leung;Yanshan Wang;Anne Olivia Raulli

  • The 2019 n2c2/UMass Lowell shared task on clinical concept normalization

    Yen-Fu Luo;Sam Henry;Yanshan Wang;Feichen Shen

  • Distilling large language models for matching patients to clinical trials

    Unknown

  • Large language models for biomedicine: foundations, opportunities, challenges, and best practices

    Unknown

  • Assessing Unmet Information Needs of Breast Cancer Survivors: Exploratory Study of Online Health Forums Using Text Classification and Retrieval

    Unknown

  • Natural language processing of radiology reports for identification of skeletal site-specific fractures

    Yanshan Wang;Saeed Mehrabi;Sunghwan Sohn;Elizabeth J. Atkinson

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