World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
50
Citations
11429
World Ranking
5566
National Ranking
2544

Chunhua Weng 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 Chunhua Weng 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: 373 publications — 84th percentile

84% of scientists in this discipline score the same or lower.

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

Chunhua Weng 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 Chunhua Weng 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: 50 D-Index — 62nd percentile

62% of scientists in this discipline score the same or lower.

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

Overview

Chunhua Weng is affiliated with Columbia University in the United States. Their research spans fields within biochemistry, genetics, molecular biology, and medicine, with a specific focus on artificial intelligence, genetics, molecular biology, public health, and econometrics. The scientist's work integrates interdisciplinary approaches involving computational methods and biomedical sciences.

Their research topics include:

  • Biomedical Text Mining and Ontologies
  • Topic Modeling
  • Machine Learning in Healthcare
  • Genomics and Rare Diseases
  • Genetic Associations and Epidemiology
  • Ethics in Clinical Research
  • Artificial Intelligence in Healthcare and Education

They have published extensively in several prominent venues, including:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Journal of the American Medical Informatics Association
  • Journal of Biomedical Informatics
  • Studies in Health Technology and Informatics
  • arXiv (Cornell University)

Frequent co-authors collaborating with Chunhua Weng are:

  • Cong Liu
  • Wendy K. Chung
  • Casey Ta
  • George Hripcsak
  • Wei-Qi Wei

Among recent publications, notable works include:

  • "Evaluating large language models on medical evidence summarization," 2023, npj Digital Medicine
  • "Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations," 2024, Nature Medicine
  • "Genome-wide polygenic score to predict chronic kidney disease across ancestries," 2022, Nature Medicine
  • "Translating evidence into practice: eligibility criteria fail to eliminate clinically significant differences between real-world and study populations," 2020, npj Digital Medicine
  • "Factors Affecting the Quality of Person-Generated Wearable Device Data and Associated Challenges: Rapid Systematic Review," 2021, JMIR mhealth and uhealth

Chunhua Weng's work frequently intersects computational and clinical domains. Their research on polygenic risk scores and genome-wide prediction models targets diverse populations, reflecting an emphasis on genetic epidemiology and precision medicine.

Their studies on large language models for medical evidence summarization and wearable device data quality further illustrate a focus on leveraging artificial intelligence and big data for healthcare applications. The scientist's contributions encompass the development and evaluation of methodologies relevant to machine learning in healthcare and the implementation of evidence-based clinical tools.

Best Publications

  • Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research.

    Nicole Gray Weiskopf;Chunhua Weng

  • Diagnostic Utility of Exome Sequencing for Kidney Disease

    Emily E Groopman;Maddalena Marasa;Sophia Cameron-Christie;Slavé Petrovski

  • A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data

    Michael G. Kahn;Tiffany J. Callahan;Juliana Barnard;Alan E. Bauck

  • Secondary Use of EHR: Data Quality Issues and Informatics Opportunities.

    Taxiarchis Botsis;Gunnar Hartvigsen;Fei Chen;Chunhua Weng

  • Defining and measuring completeness of electronic health records for secondary use

    Nicole G. Weiskopf;George Hripcsak;Sushmita Swaminathan;Chunhua Weng

  • Evaluating large language models on medical evidence summarization

    Unknown

  • Formal representation of eligibility criteria

    Chunhua Weng;Samson W. Tu;Ida Sim;Rachel Richesson

  • Electronic Screening Improves Efficiency in Clinical Trial Recruitment

    Samir R. Thadani;Samir R. Thadani;Chunhua Weng;J. Thomas Bigger;John F. Ennever

  • EliXR: an approach to eligibility criteria extraction and representation

    Chunhua Weng;Xiaoying Wu;Zhihui Luo;Mary Regina Boland

  • Combining PubMed knowledge and EHR data to develop a weighted bayesian network for pancreatic cancer prediction

    Di Zhao;Chunhua Weng

  • Criteria2Query: a natural language interface to clinical databases for cohort definition

    Chi Yuan;Chi Yuan;Patrick B. Ryan;Patrick B. Ryan;Casey N. Ta;Yixuan Guo

  • A Data Quality Assessment Guideline for Electronic Health Record Data Reuse.

    Nicole G. Weiskopf;Suzanne Bakken;George Hripcsak;Chunhua Weng

  • Hidden in plain sight: bias towards sick patients when sampling patients with sufficient electronic health record data for research

    Alexander Rusanov;Nicole Gray Weiskopf;Shuang Wang;Chunhua Weng

  • A review of auditing methods applied to the content of controlled biomedical terminologies

    Xinxin Zhu;Jung-Wei Fan;David M. Baorto;Chunhua Weng

  • Deep Phenotyping on Electronic Health Records Facilitates Genetic Diagnosis by Clinical Exomes

    Jung Hoon Son;Gangcai Xie;Chi Yuan;Lyudmila Ena

  • Translating Evidence Into Practice: Eligibility Criteria Fail to Eliminate Clinically Significant Differences Between Real-World and Study Populations

    Amelia J. Averitt;Chunhua Weng;Patrick Ryan;Patrick Ryan;Adler Perotte

  • Asynchronous collaborative writing through annotations

    Chunhua Weng;John H. Gennari

  • EliIE: An open-source information extraction system for clinical trial eligibility criteria.

    Tian Kang;Shaodian Zhang;Youlan Tang;Gregory William Hruby

  • Case-based reasoning using electronic health records efficiently identifies eligible patients for clinical trials

    Riccardo Miotto;Chunhua Weng

  • Sick patients have more data: the non-random completeness of electronic health records.

    Nicole Gray Weiskopf;Alex Rusanov;Chunhua Weng

  • Using EHRs to integrate research with patient care: promises and challenges.

    Chunhua Weng;Paul Appelbaum;George Hripcsak;Ian Kronish

  • Comparing ICD9-encoded diagnoses and NLP-processed discharge summaries for clinical trials pre-screening: a case study.

    Li Li;Herbert S. Chase;Chintan O. Patel;Carol Friedman

Frequent Co-Authors

George Hripcsak
George Hripcsak Columbia University
Wendy K. Chung
Wendy K. Chung Columbia University
Hakon Hakonarson
Hakon Hakonarson Children's Hospital of Philadelphia
Gail P. Jarvik
Gail P. Jarvik University of Washington
Suzanne Bakken
Suzanne Bakken Columbia University
Kai Wang
Kai Wang Hebei University
Ali G. Gharavi
Ali G. Gharavi Columbia University
James J. Cimino
James J. Cimino University of Alabama at Birmingham
Joshua C. Denny
Joshua C. Denny National Institutes of Health
John H. Gennari
John H. Gennari University of Washington

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