Won-Suk Oh
Impact in
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- Artificial Intelligence in Healthcare
Papers in
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- Machine Learning in Healthcare 8
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- Chronic Kidney Disease and Diabetes 2
- Acute Kidney Injury Research 2
- Co-authors
- Girish N. Nadkarni (10 shared papers)M. Regina Castro (4 shared papers)Pedro J. Caraballo (5 shared papers)György Simon (4 shared papers)Michael Steinbach (4 shared papers)Vipin Kumar (3 shared papers)Soo Borson (2 shared papers)Lili Chan (6 shared papers)
- Journals
- Critical Care (2 papers)Artificial Intelligence in Medicine (1 paper)Nutrition Metabolism and Cardiovascular Diseases (1 paper)JMIR Aging (1 paper)Journal of Biomedical Informatics (1 paper)
- Partner nations
- United StatesThailandSouth Korea
In The Last Decade
Won-Suk Oh
17 papers receiving 169 citations
Peers
Comparison fields: 5 of 59
- Health Information Management 35
- Health Informatics 5
- Nephrology 14
- Artificial Intelligence 68
- Neurology 11
Countries citing papers authored by Won-Suk Oh
This map shows the geographic impact of Won-Suk Oh'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 Won-Suk Oh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Won-Suk Oh more than expected).
Fields of papers citing papers by Won-Suk Oh
This network shows the impact of papers produced by Won-Suk Oh. 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 Won-Suk Oh. The network helps show where Won-Suk Oh may publish in the future.
Co-authors
The 25 scholars most cited alongside Won-Suk Oh, 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 21 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 41 | |
| 2 | 2023 | 34 | |
| 3 | 2024 | 25 | |
| 4 | 2015 | 15 | |
| 5 | 2022 | 11 | |
| 6 | Divisive Hierarchical Clustering towards Identifying Clinically Significant Pre-Diabetes Subpopulations. | 2014 | 9 |
| 7 | 2023 | 8 | |
| 8 | 2021 | 8 | |
| 9 | 2017 | 5 | |
| 10 | 2021 | 5 | |
| 11 | 2019 | 4 | |
| 12 | 2020 | 2 | |
| 13 | 2023 | 1 | |
| 14 | 2025 | 1 | |
| 15 | 2023 | 1 | |
| 16 | 2023 | 1 | |
| 17 | 2017 | 1 | |
| 18 | 2025 | 0 | |
| 19 | 2025 | 0 | |
| 20 | 2023 | 0 |
About Won-Suk Oh
Won-Suk Oh is a scholar working on Artificial Intelligence, Nephrology, Endocrinology, Diabetes and Metabolism, Molecular Biology and Signal Processing, having authored 21 papers that have together received 172 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (8 papers), Diabetes Management and Research (3 papers), Diabetes, Cardiovascular Risks, and Lipoproteins (3 papers), Chronic Kidney Disease and Diabetes (2 papers), Dementia and Cognitive Impairment Research (2 papers), Acute Kidney Injury Research (2 papers), Time Series Analysis and Forecasting (2 papers) and Artificial Intelligence in Healthcare (1 paper). The work is most often cited by research in Health Information Management (35 citations), Health Informatics (5 citations), Nephrology (14 citations), Artificial Intelligence (68 citations) and Neurology (11 citations). Won-Suk Oh has collaborated with scholars based in United States, Thailand and South Korea. Frequent co-authors include Girish N. Nadkarni, M. Regina Castro, Pedro J. Caraballo, György Simon, Michael Steinbach, Vipin Kumar, Soo Borson, Lili Chan, Kullaya Takkavatakarn and Ira Hofer. Their work appears in journals such as Critical Care, Artificial Intelligence in Medicine, Nutrition Metabolism and Cardiovascular Diseases, JMIR Aging and Journal of Biomedical Informatics.
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.