Daisy Yi Ding
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Machine Learning in Healthcare
- Explainable Artificial Intelligence (XAI)
Papers in
-
- Topic Modeling 2
- Explainable Artificial Intelligence (XAI) 2
- Machine Learning in Healthcare 2
- Natural Language Processing Techniques 1
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- Biomedical Text Mining and Ontologies 2
- Single-cell and spatial transcriptomics 1
- Co-authors
- Yuhao Zhang (1 shared paper)Curtis P. Langlotz (1 shared paper)Christopher D. Manning (1 shared paper)Andrew Y. Ng (1 shared paper)Anand Avati (1 shared paper)Khanh K. Thai (1 shared paper)Alejandro Schuler (1 shared paper)Sanjay Basu (1 shared paper)
- Journals
- Nature Genetics (1 paper)npj Digital Medicine (1 paper)Proceedings of the National Academy of Sciences (1 paper)NEJM AI (1 paper)PubMed (1 paper)
- Partner nations
- United StatesUnited KingdomTürkiye
In The Last Decade
Daisy Yi Ding
7 papers receiving 255 citations
Peers
Comparison fields: 5 of 84
- Health Informatics 38
- Artificial Intelligence 144
- Health Information Management 9
- Statistics, Probability and Uncertainty 11
- Computer Science Applications 7
Countries citing papers authored by Daisy Yi Ding
This map shows the geographic impact of Daisy Yi Ding'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 Daisy Yi Ding with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daisy Yi Ding more than expected).
Fields of papers citing papers by Daisy Yi Ding
This network shows the impact of papers produced by Daisy Yi Ding. 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 Daisy Yi Ding. The network helps show where Daisy Yi Ding may publish in the future.
Co-authors
The 25 scholars most cited alongside Daisy Yi Ding, 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 | 2018 | 80 | |
| 2 | 2024 | 70 | |
| 3 | NGBoost: Natural Gradient Boosting for Probabilistic Prediction | 2020 | 69 |
| 4 | 2022 | 35 | |
| 5 | 2018 | 13 | |
| 6 | 2024 | 2 | |
| 7 | 2025 | 1 |
About Daisy Yi Ding
Daisy Yi Ding is a scholar working on Artificial Intelligence, Molecular Biology, Infectious Diseases, Health Information Management and Computer Vision and Pattern Recognition, having authored 7 papers that have together received 270 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (2 papers), Topic Modeling (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Machine Learning in Healthcare (2 papers), Single-cell and spatial transcriptomics (1 paper), COVID-19 diagnosis using AI (1 paper), Natural Language Processing Techniques (1 paper) and Artificial Intelligence in Healthcare and Education (1 paper). The work is most often cited by research in Health Informatics (38 citations), Artificial Intelligence (144 citations), Health Information Management (9 citations), Statistics, Probability and Uncertainty (11 citations) and Computer Science Applications (7 citations). Daisy Yi Ding has collaborated with scholars based in United States, United Kingdom and Türkiye. Frequent co-authors include Yuhao Zhang, Curtis P. Langlotz, Christopher D. Manning, Andrew Y. Ng, Anand Avati, Khanh K. Thai, Alejandro Schuler, Sanjay Basu, Tony Duan and Balasubramanian Narasimhan. Their work appears in journals such as Nature Genetics, npj Digital Medicine, Proceedings of the National Academy of Sciences, NEJM AI and PubMed.
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.