Adarsh Subbaswamy
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
- Health Information Management top 10%
- Artificial Intelligence in Healthcare
Papers in
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- Artificial Intelligence in Healthcare and Education 1
-
- Artificial Intelligence in Healthcare 2
- Co-authors
- Suchi SariaDavid IrwinChen DongSean BarkerPrashant ShenoyRoy J. AdamsHadi KharraziDarrell J. Gaskin
- Journals
- Biostatistics (1 paper)npj Digital Medicine (1 paper)Journal of the American Medical Informatics Association (1 paper)SHILAP Revista de lepidopterología (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United States
In The Last Decade
Adarsh Subbaswamy
10 papers receiving 323 citations
Peers
Comparison fields: 5 of 87
- Health Informatics 67
- Health Information Management 26
- Building and Construction 63
- Artificial Intelligence 102
- Computer Vision and Pattern Recognition 37
Countries citing papers authored by Adarsh Subbaswamy
This map shows the geographic impact of Adarsh Subbaswamy'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 Adarsh Subbaswamy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Adarsh Subbaswamy more than expected).
Fields of papers citing papers by Adarsh Subbaswamy
This network shows the impact of papers produced by Adarsh Subbaswamy. 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 Adarsh Subbaswamy. The network helps show where Adarsh Subbaswamy may publish in the future.
Co-authorship network
The 14 scholars most cited alongside Adarsh Subbaswamy, 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 | 2024 | 6 | |
| 2 | 2024 | 1 | |
| 3 | 2022 | 10 | |
| 4 | 2022 | 36 | |
| 5 | Evaluating Model Robustness and Stability to Dataset Shift | 2021 | 2 |
| 6 | 2019 | 135 | |
| 7 | Learning Predictive Models That Transport. | 2018 | 1 |
| 8 | Counterfactual Normalization: Proactively Addressing Dataset Shift Using Causal Mechanisms. | 2018 | 3 |
| 9 | Treatment-Response Models for Counterfactual Reasoning with Continuous-time, Continuous-valued Interventions | 2017 | 3 |
| 10 | 2013 | 130 |
About Adarsh Subbaswamy
Adarsh Subbaswamy is a scholar working on Health Informatics, Health Information Management, Artificial Intelligence, Statistics and Probability and Transportation, having authored 10 papers that have together received 327 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (5 papers), Bayesian Modeling and Causal Inference (4 papers), Artificial Intelligence in Healthcare (2 papers), Statistical Methods and Inference (2 papers), Gaussian Processes and Bayesian Inference (2 papers), Smart Grid Energy Management (1 paper), Machine Learning and ELM (1 paper) and Artificial Intelligence in Healthcare and Education (1 paper). The work is most often cited by research in Health Informatics (67 citations), Health Information Management (26 citations), Building and Construction (63 citations), Artificial Intelligence (102 citations) and Computer Vision and Pattern Recognition (37 citations). Adarsh Subbaswamy has collaborated with scholars based in United States. Frequent co-authors include Suchi Saria, David Irwin, Chen Dong, Sean Barker, Prashant Shenoy, Roy J. Adams, Hadi Kharrazi, Darrell J. Gaskin, Bryant Chen and Berkman Sahiner. Their work appears in journals such as Biostatistics, npj Digital Medicine, Journal of the American Medical Informatics Association, SHILAP Revista de lepidopterología and arXiv (Cornell University).
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.