Yu Tian
- Health Informatics top 2%
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- Artificial Intelligence in Healthcare 16
- Electronic Health Records Systems 8
- Artificial Intelligence top 5%
- Machine Learning in Healthcare 21
- AI in cancer detection 12
- Sensory Systems top 10%
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- Biomedical Text Mining and Ontologies 10
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- Radiomics and Machine Learning in Medical Imaging 9
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- Advanced Neural Network Applications 6
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- Colorectal Cancer Screening and Detection 6
- Journals
- SHILAP Revista de lepidopterología (2 papers)Journal of Agricultural and Food Chemistry (1 paper)Scientific Reports (1 paper)
- Partner nations
- ChinaUnited StatesItaly
In The Last Decade
Yu Tian
101 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 164
- Health Informatics 53
- Health Information Management 158
- Artificial Intelligence 335
- Cancer Research 109
- Sensory Systems 35
Countries citing papers authored by Yu Tian
This map shows the geographic impact of Yu Tian'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 Yu Tian with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yu Tian more than expected).
Fields of papers citing papers by Yu Tian
This network shows the impact of papers produced by Yu Tian. 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 Yu Tian. The network helps show where Yu Tian may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Yu Tian, 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 | 2025 | 1 | |
| 2 | 2024 | 12 | |
| 3 | 2024 | 0 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 10 | |
| 6 | 2024 | 0 | |
| 7 | 2024 | 1 | |
| 8 | 2024 | 1 | |
| 9 | 2024 | 4 | |
| 10 | 2023 | 1 | |
| 11 | 2023 | 3 | |
| 12 | 2022 | 5 | |
| 13 | 2021 | 7 | |
| 14 | 2021 | 12 | |
| 15 | 2021 | 10 | |
| 16 | 2021 | 5 | |
| 17 | A New Immunological Prognostic Model Based on Immunohistochemistry for Extranodal Natural Killer/T-Cell Lymphoma Patients After Non-Anthracycline-Based Chemotherapy | 2020 | 1 |
| 18 | 2020 | 46 | |
| 19 | 2020 | 62 | |
| 20 | 2019 | 12 |
About Yu Tian
Yu Tian is a scholar working on Health Information Management, Health Informatics and Artificial Intelligence, having authored 112 papers that have together received 1.3k indexed citations. Recurring topics across this work include Machine Learning in Healthcare (21 papers), Artificial Intelligence in Healthcare (16 papers), AI in cancer detection (12 papers), Biomedical Text Mining and Ontologies (10 papers), Radiomics and Machine Learning in Medical Imaging (9 papers), Electronic Health Records Systems (8 papers), Advanced Neural Network Applications (6 papers) and Colorectal Cancer Screening and Detection (6 papers). The work is most often cited by research in Health Informatics (53 citations), Health Information Management (158 citations) and Artificial Intelligence (335 citations). Yu Tian has collaborated with scholars based in China, United States and Italy. Frequent co-authors include Jingsong Li, Tianshu Zhou, Kefeng Ding, Yu Wang, Pengfei Li, Lili Tian, Jingjing Ren, Xiangxing Kong, Mao Zhang and Tingbo Liang. Their work appears in journals such as SHILAP Revista de lepidopterología, Journal of Agricultural and Food Chemistry and Scientific Reports.
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.