Shuo Yan

625 citations
25 papers · 457 indexed · h-index 11

Impact in

  • Neurology top 10%
    • Traumatic Brain Injury and Neurovascular Disturbances
    • Long-Term Effects of COVID-19
    • COVID-19 Clinical Research Studies

Papers in

Shuo Yan

24 papers receiving 449 citations

Peers

Shuo Yan
Comparison fields: 5 of 83
  • Neurology 112
  • Infectious Diseases 134
  • Critical Care and Intensive Care Medicine 36
  • Radiology, Nuclear Medicine and Imaging 157
  • Internal Medicine 18
Replace Katarzyna Sklinda with:
Katarzyna Sklinda Poland
Xiaojia Wu China
Rebecca Preston United Kingdom
Alessandra Bindi Italy
Qingyun Long China
Amit Aggarwal United States
Alice Le Berre France
Maurizio Viecca Italy
Hamid Rajebi United States
Shuo Yan relative to Katarzyna Sklinda Poland Katarzyna Sklinda's profile →
Citations per field
00.5×4.1×
Katarzyna Sklinda · 1×
Citations per year

Countries citing papers authored by Shuo Yan

Since Specialization
Citations

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

Fields of papers citing papers by Shuo Yan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Shuo Yan, 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 Shuo Yan Line = papers co-authored together Shuo Yan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 202110
3 20215
4 20212
5 20214
6 20206
7 202024
8 20207
9 2020140
10 202019
11 202012
12 20208
13 20204
14 20185
15 201510
16 201452
17 201427
18 201434
19 201324
20 201338

About Shuo Yan

Shuo Yan is a scholar working on Critical Care and Intensive Care Medicine, Internal Medicine, Neurology, Radiology, Nuclear Medicine and Imaging and Otorhinolaryngology, having authored 25 papers that have together received 457 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (3 papers), COVID-19 Clinical Research Studies (3 papers), Advanced X-ray and CT Imaging (3 papers), Acute Ischemic Stroke Management (3 papers), Ultrasound in Clinical Applications (3 papers), COVID-19 diagnosis using AI (3 papers), MRI in cancer diagnosis (2 papers) and CNS Lymphoma Diagnosis and Treatment (2 papers). The work is most often cited by research in Neurology (112 citations), Infectious Diseases (134 citations), Critical Care and Intensive Care Medicine (36 citations), Radiology, Nuclear Medicine and Imaging (157 citations) and Internal Medicine (18 citations). Shuo Yan has collaborated with scholars based in China, Singapore and United Kingdom. Frequent co-authors include Hui Chen, Yan Du, Hang Jin, Yingqi Xing, Yi Yang, Zhen‐Ni Guo, Jia Liu, Ruming Xie, Shouchun Wang and Shuang Xia. Their work appears in journals such as Medicine, BMC Infectious Diseases, PLoS ONE, International Journal of Surgery and Academic Radiology.

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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