Bo Jin
Impact in
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- Artificial Intelligence in Healthcare
- Artificial Intelligence top 2%
- Machine Learning in Healthcare
- Topic Modeling
- Imbalanced Data Classification Techniques
Papers in
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- Topic Modeling 12
- Machine Learning in Healthcare 10
- Advanced Graph Neural Networks 5
- Natural Language Processing Techniques 4
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- Time Series Analysis and Forecasting 9
- Co-authors
- Chao Che (12 shared papers)Yue Qu (8 shared papers)Peiliang Zhang (3 shared papers)Liang Zhang (7 shared papers)Xiaopeng Wei (13 shared papers)Haoyu Yang (3 shared papers)Chuanren Liu (5 shared papers)Lin Feng (7 shared papers)
- Journals
- IEEE Access (6 papers)BMC Medical Informatics and Decision Making (4 papers)IEEE Transactions on Knowledge and Data Engineering (3 papers)Sensors (3 papers)Journal of Medical Internet Research (2 papers)
- Partner nations
- ChinaUnited StatesEthiopia
In The Last Decade
Bo Jin
76 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 122
- Health Information Management 129
- Artificial Intelligence 480
- Signal Processing 152
- Health Informatics 18
- Information Systems 230
Countries citing papers authored by Bo Jin
This map shows the geographic impact of Bo Jin'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 Bo Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bo Jin more than expected).
Fields of papers citing papers by Bo Jin
This network shows the impact of papers produced by Bo Jin. 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 Bo Jin. The network helps show where Bo Jin may publish in the future.
Co-authors
The 25 scholars most cited alongside Bo Jin, 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 87 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 117 | |
| 2 | 2018 | 116 | |
| 3 | 2020 | 75 | |
| 4 | 2018 | 59 | |
| 5 | 2018 | 53 | |
| 6 | 2016 | 50 | |
| 7 | 2017 | 44 | |
| 8 | 2020 | 43 | |
| 9 | 2017 | 41 | |
| 10 | 2016 | 40 | |
| 11 | 2018 | 40 | |
| 12 | 2016 | 40 | |
| 13 | 2012 | 38 | |
| 14 | 2020 | 30 | |
| 15 | 2021 | 30 | |
| 16 | 2021 | 27 | |
| 17 | 2023 | 25 | |
| 18 | 2023 | 22 | |
| 19 | 2016 | 18 | |
| 20 | 2013 | 17 |
About Bo Jin
Bo Jin is a scholar working on Artificial Intelligence, Signal Processing, Information Systems, Molecular Biology and Computer Networks and Communications, having authored 87 papers that have together received 1.2k indexed citations. Recurring topics across this work include Topic Modeling (12 papers), Machine Learning in Healthcare (10 papers), Time Series Analysis and Forecasting (9 papers), Computational Drug Discovery Methods (6 papers), EEG and Brain-Computer Interfaces (5 papers), Advanced Graph Neural Networks (5 papers), Artificial Intelligence in Healthcare (5 papers) and Natural Language Processing Techniques (4 papers). The work is most often cited by research in Health Information Management (129 citations), Artificial Intelligence (480 citations), Signal Processing (152 citations), Health Informatics (18 citations) and Information Systems (230 citations). Bo Jin has collaborated with scholars based in China, United States and Ethiopia. Frequent co-authors include Chao Che, Yue Qu, Peiliang Zhang, Liang Zhang, Xiaopeng Wei, Haoyu Yang, Chuanren Liu, Lin Feng, Xiaomeng Yin and Zhen Liu. Their work appears in journals such as IEEE Access, BMC Medical Informatics and Decision Making, IEEE Transactions on Knowledge and Data Engineering, Sensors and Journal of Medical Internet Research.
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.