Shenda Hong
- Cardiology and Cardiovascular Medicine top 5%
- Artificial Intelligence top 2%
- Cognitive Neuroscience top 5%
- Computer Vision and Pattern Recognition top 5%
- Biomedical Engineering top 10%
- Topics
- ECG Monitoring and Analysis (32 papers)EEG and Brain-Computer Interfaces (19 papers)Machine Learning in Healthcare (16 papers)
- Journals
- Proceedings of the National Academy of SciencesNature CommunicationsSHILAP Revista de lepidopterología
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Shenda Hong
92 papers receiving 2.1k citations
Hit Papers
Peers
Comparison fields: 5 of 163
- Cardiology and Cardiovascular Medicine 655
- Artificial Intelligence 578
- Cognitive Neuroscience 470
- Computer Vision and Pattern Recognition 340
- Biomedical Engineering 281
Countries citing papers authored by Shenda Hong
This map shows the geographic impact of Shenda Hong'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 Shenda Hong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shenda Hong more than expected).
Fields of papers citing papers by Shenda Hong
This network shows the impact of papers produced by Shenda Hong. 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 Shenda Hong. The network helps show where Shenda Hong may publish in the future.
Co-authorship network of co-authors of Shenda Hong
This figure shows the co-authorship network connecting the top 25 collaborators of Shenda Hong. A scholar is included among the top collaborators of Shenda Hong based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Shenda Hong. Shenda Hong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 4 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 11 | |
| 7 | 6 | |
| 8 | 2 | |
| 9 | 34 | |
| 10 | 4 | |
| 11 | 6 | |
| 12 | 12 | |
| 13 | 3 | |
| 14 | 0 | |
| 15 | 1 | |
| 16 | 26 | |
| 17 | 6 | |
| 18 | Knowledge Guided Multi-instance Multi-label Learning via Neural Networks in Medicines Prediction | 3 |
| 19 | 107 | |
| 20 | A Novel Method for Mining Semantics from Patterns over ECG Data. | 2 |
About Shenda Hong
Shenda Hong is a scholar working on Health Informatics, Cardiology and Cardiovascular Medicine and Health Information Management, having authored 100 papers that have together received 2.2k indexed citations. Recurring topics across this work include ECG Monitoring and Analysis (32 papers), EEG and Brain-Computer Interfaces (19 papers) and Machine Learning in Healthcare (16 papers). The work is most often cited by research in Health Informatics (60 citations), Cardiology and Cardiovascular Medicine (655 citations) and Cognitive Neuroscience (470 citations). Shenda Hong has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Yuxi Zhou, Hongyan Li, Junyuan Shang, Jimeng Sun, L. Yang, Zhilong Zhang, Bin Cui, Wentao Zhang, Yang Song and Runsheng Xu. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nature Communications and SHILAP Revista de lepidopterología.
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.