Tsung-Ting Kuo
- Health Informatics top 1%
- Information Systems top 0.5%
- Blockchain Technology Applications and Security 13
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- IoT and Edge/Fog Computing 6
- Artificial Intelligence top 2%
- Privacy-Preserving Technologies in Data 12
- Topic Modeling 7
- Machine Learning in Healthcare 5
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- Complex Network Analysis Techniques 7
- Opinion Dynamics and Social Influence 6
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- Biomedical Text Mining and Ontologies 6
- Co-authors
- Lucila Ohno‐MachadoHyeoneui KimRodney A. GabrielShou-De LinChun‐Nan HsuRahul KashyapShitij BhargavaDennis Grishin
- Partner nations
- United StatesTaiwanUnited Kingdom
In The Last Decade
Tsung-Ting Kuo
49 papers receiving 1.9k citations
Hit Papers
Peers
Comparison fields: 5 of 140
- Health Informatics 108
- Information Systems 1.1k
- Computer Networks and Communications 503
- Management Information Systems 168
- Artificial Intelligence 574
Countries citing papers authored by Tsung-Ting Kuo
This map shows the geographic impact of Tsung-Ting Kuo'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 Tsung-Ting Kuo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tsung-Ting Kuo more than expected).
Fields of papers citing papers by Tsung-Ting Kuo
This network shows the impact of papers produced by Tsung-Ting Kuo. 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 Tsung-Ting Kuo. The network helps show where Tsung-Ting Kuo may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Tsung-Ting Kuo, 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 | 2025 | 0 | |
| 2 | 2025 | 5 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 2 | |
| 5 | 2024 | 2 | |
| 6 | 2024 | 3 | |
| 7 | 2023 | 2 | |
| 8 | 2022 | 13 | |
| 9 | 2021 | 38 | |
| 10 | 2021 | 10 | |
| 11 | 2021 | 18 | |
| 12 | 2019 | 49 | |
| 13 | 2019 | 187 | |
| 14 | 2018 | 15 | |
| 15 | The presence of highly similar notes within the MIMIC-III dataset. | 2017 | 1 |
| 16 | 2017 | 1 | |
| 17 | 2016 | 149 | |
| 18 | Exploiting Latent Information to Predict Diffusions of Novel Topics on Social Networks | 2012 | 11 |
| 19 | Feature Engineering and Classifier Ensemble for KDD Cup 2010 | 2010 | 89 |
| 20 | An ensemble of three classifiers for KDD cup 2009: expanded linear model, heterogeneous boosting, and selective naïve Bayes | 2009 | 11 |
About Tsung-Ting Kuo
Tsung-Ting Kuo is a scholar working on Health Informatics, Artificial Intelligence and Information Systems, having authored 50 papers that have together received 1.9k indexed citations. Recurring topics across this work include Blockchain Technology Applications and Security (13 papers), Privacy-Preserving Technologies in Data (12 papers), Topic Modeling (7 papers), Complex Network Analysis Techniques (7 papers), IoT and Edge/Fog Computing (6 papers), Biomedical Text Mining and Ontologies (6 papers), Opinion Dynamics and Social Influence (6 papers) and Machine Learning in Healthcare (5 papers). The work is most often cited by research in Health Informatics (108 citations), Information Systems (1.1k citations) and Computer Networks and Communications (503 citations). Tsung-Ting Kuo has collaborated with scholars based in United States, Taiwan and United Kingdom. Frequent co-authors include Lucila Ohno‐Machado, Hyeoneui Kim, Rodney A. Gabriel, Shou-De Lin, Chun‐Nan Hsu, Rahul Kashyap, Shitij Bhargava, Dennis Grishin, Tim K. Mackey and Robert Barkovich. Their work appears in journals such as Nature Communications, Scientific Reports and BMC Bioinformatics.
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.