Ying Sha
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
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- Artificial Intelligence in Healthcare
Papers in
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- Machine Learning in Healthcare 4
- Explainable Artificial Intelligence (XAI) 3
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- Web Data Mining and Analysis 2
- Co-authors
- May D. Wang (9 shared papers)John H. Phan (1 shared paper)Mohammed Saqib (1 shared paper)Tong Li (3 shared papers)Felipe Giuste (2 shared papers)Monica Isgut (2 shared papers)Yuanda Zhu (2 shared papers)Wenqi Shi (1 shared paper)
- Journals
- Medicine (1 paper)Progress in Organic Coatings (1 paper)IEEE Journal of Biomedical and Health Informatics (1 paper)IEEE Reviews in Biomedical Engineering (1 paper)BMC Women s Health (1 paper)
- Partner nations
- ChinaUnited StatesKazakhstan
In The Last Decade
Ying Sha
20 papers receiving 435 citations
Peers
Comparison fields: 5 of 120
- Health Informatics 50
- Health Information Management 38
- Family Practice 12
- Artificial Intelligence 170
- Radiology, Nuclear Medicine and Imaging 44
Countries citing papers authored by Ying Sha
This map shows the geographic impact of Ying Sha'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 Ying Sha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ying Sha more than expected).
Fields of papers citing papers by Ying Sha
This network shows the impact of papers produced by Ying Sha. 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 Ying Sha. The network helps show where Ying Sha may publish in the future.
Co-authors
The 25 scholars most cited alongside Ying Sha, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 91 | |
| 2 | 2015 | 82 | |
| 3 | 2017 | 75 | |
| 4 | 2018 | 49 | |
| 5 | 2016 | 29 | |
| 6 | 2015 | 25 | |
| 7 | 2018 | 15 | |
| 8 | 2019 | 13 | |
| 9 | 2016 | 11 | |
| 10 | 2021 | 10 | |
| 11 | 2023 | 10 | |
| 12 | 2018 | 8 | |
| 13 | 2022 | 8 | |
| 14 | 2021 | 6 | |
| 15 | 2024 | 3 | |
| 16 | 2023 | 2 | |
| 17 | 2022 | 2 | |
| 18 | 2016 | 2 | |
| 19 | Algorithm of Direct Multi-string Matching to Anti-spam | 2005 | 1 |
| 20 | 2013 | 1 |
About Ying Sha
Ying Sha is a scholar working on Artificial Intelligence, Information Systems, Molecular Biology, Signal Processing and Radiology, Nuclear Medicine and Imaging, having authored 21 papers that have together received 443 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (4 papers), Explainable Artificial Intelligence (XAI) (3 papers), COVID-19 diagnosis using AI (2 papers), Web Data Mining and Analysis (2 papers), Pharmacological Effects of Natural Compounds (2 papers), Artificial Intelligence in Healthcare and Education (2 papers), Nuclear materials and radiation effects (1 paper) and Gene expression and cancer classification (1 paper). The work is most often cited by research in Health Informatics (50 citations), Health Information Management (38 citations), Family Practice (12 citations), Artificial Intelligence (170 citations) and Radiology, Nuclear Medicine and Imaging (44 citations). Ying Sha has collaborated with scholars based in China, United States and Kazakhstan. Frequent co-authors include May D. Wang, John H. Phan, Mohammed Saqib, Tong Li, Felipe Giuste, Monica Isgut, Yuanda Zhu, Wenqi Shi, Rui Li and Janani Venugopalan. Their work appears in journals such as Medicine, Progress in Organic Coatings, IEEE Journal of Biomedical and Health Informatics, IEEE Reviews in Biomedical Engineering and BMC Women s Health.
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.