Chunhua Weng

12.6k total citations · 2 hit papers
284 papers, 6.1k citations indexed

About

Chunhua Weng is a scholar working on Molecular Biology, Artificial Intelligence and Public Health, Environmental and Occupational Health. According to data from OpenAlex, Chunhua Weng has authored 284 papers receiving a total of 6.1k indexed citations (citations by other indexed papers that have themselves been cited), including 132 papers in Molecular Biology, 122 papers in Artificial Intelligence and 51 papers in Public Health, Environmental and Occupational Health. Recurrent topics in Chunhua Weng's work include Biomedical Text Mining and Ontologies (123 papers), Topic Modeling (55 papers) and Machine Learning in Healthcare (53 papers). Chunhua Weng is often cited by papers focused on Biomedical Text Mining and Ontologies (123 papers), Topic Modeling (55 papers) and Machine Learning in Healthcare (53 papers). Chunhua Weng collaborates with scholars based in United States, Canada and China. Chunhua Weng's co-authors include Nicole G. Weiskopf, George Hripcsak, Alexander Rusanov, Riccardo Miotto, Patrick Ryan, Mary Regina Boland, John H. Gennari, Michael G. Kahn, Gunnar Hartvigsen and Taxiarchis Botsis and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Nature Genetics.

In The Last Decade

Chunhua Weng

266 papers receiving 6.0k citations

Hit Papers

align trajectories sort by overperformance

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.

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

2012 757 citations Chunhua Weng et al. Journal of the American Medical Informatics Association profile →
A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data

2016 347 citations Patrick Ryan, Ning Shang et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chunhua Weng United States	36	2.2k	1.9k	1.5k	1.1k	682	284	6.1k
Patrick Ryan United States	49	1.3k 0.6×	1.4k 0.7×	1.1k 0.7×	870 0.8×	441 0.6×	217	8.7k
Bradley Malin United States	42	3.3k 1.5×	716 0.4×	859 0.6×	1.5k 1.4×	816 1.2×	279	6.5k
Jyotishman Pathak United States	40	1.8k 0.8×	1.3k 0.7×	905 0.6×	625 0.6×	225 0.3×	232	5.6k
Martijn J. Schuemie United States	48	1.5k 0.7×	1.8k 0.9×	707 0.5×	559 0.5×	239 0.4×	194	8.5k
Hongfang Liu United States	42	3.7k 1.7×	3.5k 1.8×	1.0k 0.7×	509 0.5×	261 0.4×	402	8.4k
Adam Wright United States	47	1.2k 0.5×	1.4k 0.7×	3.6k 2.4×	1.2k 1.1×	495 0.7×	281	7.8k
Wendy W. Chapman United States	40	3.8k 1.8×	2.9k 1.5×	908 0.6×	545 0.5×	340 0.5×	167	7.4k
Christopher G. Chute United States	59	4.8k 2.2×	5.5k 2.8×	2.0k 1.3×	1.4k 1.3×	568 0.8×	351	14.9k
Hua Xu United States	48	4.4k 2.0×	4.4k 2.3×	1.0k 0.7×	375 0.3×	321 0.5×	399	9.1k
William Hersh United States	50	2.9k 1.3×	2.7k 1.4×	2.1k 1.4×	1.2k 1.1×	588 0.9×	263	8.3k

Countries citing papers authored by Chunhua Weng

Since Specialization

Citations

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

Fields of papers citing papers by Chunhua Weng

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chunhua Weng

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

All Works

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20 of 20 papers shown

Zhang, Gongbo, et al.. (2025). Semi-supervised learning from small annotated data and large unlabeled data for fine-grained Participants, Intervention, Comparison, and Outcomes entity recognition. Journal of the American Medical Informatics Association. 32(3). 555–565. 3 indexed citations

Park, Jimyung, Casey Ta, Betina Idnay, et al.. (2024). Criteria2Query 3.0: Leveraging generative large language models for clinical trial eligibility query generation. Journal of Biomedical Informatics. 154. 104649–104649. 17 indexed citations

Chung, Wendy K., et al.. (2024). Rare disease diagnosis using knowledge guided retrieval augmentation for ChatGPT. Journal of Biomedical Informatics. 157. 104702–104702. 11 indexed citations

Ahimaz, Priyanka, Nga Nguyen, Rachel Lewis, et al.. (2024). Phenotype driven molecular genetic test recommendation for diagnosing pediatric rare disorders. npj Digital Medicine. 7(1). 333–333. 2 indexed citations

Liu, Cong, et al.. (2024). Fine-tuning large language models for rare disease concept normalization. Journal of the American Medical Informatics Association. 31(9). 2076–2083. 17 indexed citations

Idnay, Betina, Gongbo Zhang, Casey Ta, et al.. (2024). Mini-mental status examination phenotyping for Alzheimer’s disease patients using both structured and narrative electronic health record features. Journal of the American Medical Informatics Association. 32(1). 119–128. 1 indexed citations

Yang, Jingye, et al.. (2023). Enhancing phenotype recognition in clinical notes using large language models: PhenoBCBERT and PhenoGPT. Patterns. 5(1). 100887–100887. 28 indexed citations

Idnay, Betina, et al.. (2023). Clinical research staff perceptions on a natural language processing-driven tool for eligibility prescreening: An iterative usability assessment. International Journal of Medical Informatics. 171. 104985–104985. 2 indexed citations

Bakken, Suzanne, John Lynch, Wendy K. Chung, et al.. (2023). Participant-guided development of bilingual genomic educational infographics for Electronic Medical Records and Genomics Phase IV study. Journal of the American Medical Informatics Association. 31(2). 306–316. 2 indexed citations

10.

Hui, Daniel, Scott Dudek, Krzysztof Kiryluk, et al.. (2023). Risk factors affecting polygenic score performance across diverse cohorts. eLife. 12.

11.

Singhal, Pankhuri, Yogasudha Veturi, Scott Dudek, et al.. (2023). Evidence of epistasis in regions of long-range linkage disequilibrium across five complex diseases in the UK Biobank and eMERGE datasets. The American Journal of Human Genetics. 110(4). 575–591. 9 indexed citations

12.

Khan, Atlas, Ning Shang, Jordan G. Nestor, et al.. (2023). Polygenic Risk Affects the Penetrance of Monogenic Kidney Disease. Journal of the American Society of Nephrology. 34(11S). 661–661.

13.

Liu, Hao, Yifan Peng, & Chunhua Weng. (2023). How Good Is ChatGPT for Medication Evidence Synthesis?. Studies in health technology and informatics. 302. 1062–1066. 4 indexed citations

14.

Ta, Casey, et al.. (2022). Clinical and temporal characterization of COVID-19 subgroups using patient vector embeddings of electronic health records. Journal of the American Medical Informatics Association. 30(2). 256–272. 4 indexed citations

15.

Juhn, Young J., Euijung Ryu, Chung‐Il Wi, et al.. (2022). Assessing socioeconomic bias in machine learning algorithms in health care: a case study of the HOUSES index. Journal of the American Medical Informatics Association. 29(7). 1142–1151. 34 indexed citations

16.

Kim, Jae Hyun, May Hua, Robert A. Whittington, et al.. (2022). A machine learning approach to identifying delirium from electronic health records. JAMIA Open. 5(2). ooac042–ooac042. 6 indexed citations

17.

Feng, Yen‐Chen Anne, Ian B. Stanaway, John J. Connolly, et al.. (2022). Psychiatric manifestations of rare variation in medically actionable genes: a PheWAS approach. BMC Genomics. 23(1). 385–385. 4 indexed citations

18.

Liu, Cong, Jae Hyun Kim, Ning Shang, et al.. (2021). Comparative effectiveness of medical concept embedding for feature engineering in phenotyping. JAMIA Open. 4(2). ooab028–ooab028. 10 indexed citations

19.

Wang, Chen, Atlas Khan, Ning Shang, et al.. (2021). Quantitative disease risk scores from EHR with applications to clinical risk stratification and genetic studies. npj Digital Medicine. 4(1). 116–116. 10 indexed citations

20.

Havrilla, James M., Fang Li, Ying Chen, et al.. (2020). Phen2Gene: rapid phenotype-driven gene prioritization for rare diseases. NAR Genomics and Bioinformatics. 2(2). lqaa032–lqaa032. 44 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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