Hui Han

873 citations
29 papers · 687 · h-index 11

Impact in

Papers in

Hui Han

26 papers receiving 649 citations

Peers

Hui Han
Comparison fields: 5 of 75
  • Cardiology and Cardiovascular Medicine 268
  • Biological Psychiatry 13
  • Endocrinology, Diabetes and Metabolism 88
  • Pharmacology 68
  • Parasitology 28
Replace Rafael Sobrano Fais with:
Rafael Sobrano Fais Brazil
Masashi Okuro Japan
Pamela A. Marks–Shulman United States
Lesya Zaichenko United States
Qiuhua Shen United States
Eemil Partinen Finland
Noboru Mesaki Japan
M.-J. Meile France
Michael Kutner United States
P. Zöfel Germany
Hui Han relative to Rafael Sobrano Fais Brazil Rafael Sobrano Fais's profile →
Citations per field
00.5×2×3×4×4.9×
Rafael Sobrano Fais · 1×
Citations per year

Countries citing papers authored by Hui Han

Since Specialization
Citations

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

Fields of papers citing papers by Hui Han

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Hui Han, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Hui Han Line = papers co-authored together Hui Han links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2002159
2 201793
3 200764
4 200460
5 200455
6 200553
7 200743
8 201731
9 202028
10 200613
11 202211
12 202411
13 20209
14 20259
15 20218
16 20197
17 20197
18 20225
19 20194
20 20234

About Hui Han

Hui Han is a scholar working on Cardiology and Cardiovascular Medicine, Health Information Management, Emergency Medical Services, Pulmonary and Respiratory Medicine and Rheumatology, having authored 29 papers that have together received 687 indexed citations. Recurring topics across this work include Cardiovascular Function and Risk Factors (4 papers), Data-Driven Disease Surveillance (3 papers), Global Health and Epidemiology (3 papers), Viral Infections and Outbreaks Research (2 papers), Cardiac Valve Diseases and Treatments (2 papers), Patient Safety and Medication Errors (2 papers), Systemic Lupus Erythematosus Research (2 papers) and RNA modifications and cancer (2 papers). The work is most often cited by research in Cardiology and Cardiovascular Medicine (268 citations), Biological Psychiatry (13 citations), Endocrinology, Diabetes and Metabolism (88 citations), Pharmacology (68 citations) and Parasitology (28 citations). Hui Han has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Herman A. Taylor, Ervin R. Fox, Violetta Klimek, Gregory A. Ordway, Craig A. Stockmeier, Tandaw E. Samdarshi, Thomas N. Skelton, Robert J. Garrison, Donna K. Arnett and Michael E. Andrew. Their work appears in journals such as American Heart Journal, The American Journal of Cardiology, Hypertension, Clinica Chimica Acta and BMJ Open.

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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