Dexiang Yang
Impact in
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- Artificial Intelligence in Healthcare and Education
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- COVID-19 Clinical Research Studies
Papers in
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- COVID-19 Clinical Research Studies 3
- SARS-CoV-2 and COVID-19 Research 2
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- Lung Cancer Diagnosis and Treatment 1
- Co-authors
- Jian Li (2 shared papers)Min Zhou (3 shared papers)Yun Ling (2 shared papers)Kui Liu (1 shared paper)Zenghui Cheng (1 shared paper)Tao Bai (1 shared paper)Ranran Dai (1 shared paper)Ping Fang (1 shared paper)
- Journals
- Epidemiology and Infection (1 paper)Saudi Journal of Biological Sciences (1 paper)Annals of Translational Medicine (1 paper)Journal of Diabetes (1 paper)SSRN Electronic Journal (1 paper)
- Partner nations
- ChinaUnited States
In The Last Decade
Dexiang Yang
6 papers receiving 62 citations
Peers
Comparison fields: 5 of 47
- Health Informatics 8
- Infectious Diseases 25
- Modeling and Simulation 5
- Radiology, Nuclear Medicine and Imaging 25
- Health Information Management 4
Countries citing papers authored by Dexiang Yang
This map shows the geographic impact of Dexiang Yang'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 Dexiang Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dexiang Yang more than expected).
Fields of papers citing papers by Dexiang Yang
This network shows the impact of papers produced by Dexiang Yang. 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 Dexiang Yang. The network helps show where Dexiang Yang may publish in the future.
Co-authors
The 25 scholars most cited alongside Dexiang Yang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 27 | |
| 2 | 2021 | 19 | |
| 3 | 2020 | 8 | |
| 4 | 2017 | 6 | |
| 5 | 2023 | 2 | |
| 6 | Clinical characteristics of 14 cases of Chlamydia psittaci pneumonia. | 2024 | 1 |
| 7 | A Deep Learning Pipeline for Accurate Differential Diagnosis between Novel Coronavirus Pneumonia and Influenza Pneumonia | 2020 | 0 |
About Dexiang Yang
Dexiang Yang is a scholar working on Infectious Diseases, Pulmonary and Respiratory Medicine, Epidemiology, Radiology, Nuclear Medicine and Imaging and Surgery, having authored 7 papers that have together received 63 indexed citations. Recurring topics across this work include COVID-19 Clinical Research Studies (3 papers), COVID-19 diagnosis using AI (2 papers), SARS-CoV-2 and COVID-19 Research (2 papers), Long-Term Effects of COVID-19 (1 paper), Lung Cancer Diagnosis and Treatment (1 paper), COVID-19 epidemiological studies (1 paper), Radiomics and Machine Learning in Medical Imaging (1 paper) and Reproductive tract infections research (1 paper). The work is most often cited by research in Health Informatics (8 citations), Infectious Diseases (25 citations), Modeling and Simulation (5 citations), Radiology, Nuclear Medicine and Imaging (25 citations) and Health Information Management (4 citations). Dexiang Yang has collaborated with scholars based in China and United States. Frequent co-authors include Jian Li, Min Zhou, Yun Ling, Kui Liu, Zenghui Cheng, Tao Bai, Ranran Dai, Ping Fang, Qi Zhang and Xiaoyan Jin. Their work appears in journals such as Epidemiology and Infection, Saudi Journal of Biological Sciences, Annals of Translational Medicine, Journal of Diabetes and SSRN Electronic Journal.
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.