Tsung-Ting Kuo

3.9k total citations · 1 hit paper
50 papers, 1.9k citations indexed

About

Tsung-Ting Kuo is a scholar working on Artificial Intelligence, Information Systems and Molecular Biology. According to data from OpenAlex, Tsung-Ting Kuo has authored 50 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Artificial Intelligence, 17 papers in Information Systems and 7 papers in Molecular Biology. Recurrent topics in Tsung-Ting Kuo's work include Blockchain Technology Applications and Security (13 papers), Privacy-Preserving Technologies in Data (12 papers) and Topic Modeling (7 papers). Tsung-Ting Kuo is often cited by papers focused on Blockchain Technology Applications and Security (13 papers), Privacy-Preserving Technologies in Data (12 papers) and Topic Modeling (7 papers). Tsung-Ting Kuo collaborates with scholars based in United States, Taiwan and United Kingdom. Tsung-Ting Kuo's 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 and has published in prestigious journals such as Nature Communications, Scientific Reports and BMC Bioinformatics.

In The Last Decade

Tsung-Ting Kuo

49 papers receiving 1.9k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Blockchain distributed ledger technologies for biomedical and health care applications

2017 736 citations Tsung-Ting Kuo, Lucila Ohno‐Machado et al. Journal of the American Medical Informatics Association profile →

Peers

Tsung-Ting Kuo

CNC

MIS

RNMI

Rachid Benlamri Canada

Ting Wang China

Adam Perer United States

Ashutosh Dhar Dwivedi Denmark

Vassilios S. Verykios Greece

Toon Calders Belgium

Madini O. Alassafi Saudi Arabia

Martin J. O’Connor United States

Deris Stiawan Indonesia

Yanyan Lan China

Rachid Benlamri Canada

Tsung-Ting Kuo

632 ×0.6

345 ×0.6

349 ×0.7

CNC

66 ×0.4

MIS

87 ×0.5

RNMI

Citations per year, relative to Tsung-Ting Kuo Tsung-Ting Kuo (= 1×) peers Rachid Benlamri

Countries citing papers authored by Tsung-Ting Kuo

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of Tsung-Ting Kuo

This figure shows the co-authorship network connecting the top 25 collaborators of Tsung-Ting Kuo. A scholar is included among the top collaborators of Tsung-Ting Kuo 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 Tsung-Ting Kuo. Tsung-Ting Kuo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Kuo, Tsung-Ting, et al.. (2025). False-positive tolerant model misconduct mitigation in distributed federated learning on electronic health record data across clinical institutions. Scientific Reports. 15(1). 23310–23310.

Kuo, Tsung-Ting, et al.. (2025). Distributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals. Nature Communications. 16(1). 1371–1371. 5 indexed citations

Yu, Yufei, et al.. (2024). Distributed, immutable, and transparent biomedical limited data set request management on multi-capacity network. Journal of the American Medical Informatics Association. 32(2). 296–307. 1 indexed citations

Zhang, Min & Tsung-Ting Kuo. (2024). Early prediction of long hospital stay for Intensive Care units readmission patients using medication information. Computers in Biology and Medicine. 174. 108451–108451. 2 indexed citations

Kuo, Tsung-Ting, et al.. (2024). WebQuorumChain: A web framework for quorum-based health care model learning. Informatics in Medicine Unlocked. 50. 101590–101590. 2 indexed citations

Kuo, Tsung-Ting, et al.. (2024). Distributed management of patient data-sharing informed consents for clinical research. Computers in Biology and Medicine. 180. 108956–108956. 3 indexed citations

Ohno‐Machado, Lucila, Xiaoqian Jiang, Tsung-Ting Kuo, et al.. (2023). A hierarchical strategy to minimize privacy risk when linking “De-identified” data in biomedical research consortia. Journal of Biomedical Informatics. 139. 104322–104322. 2 indexed citations

Kuo, Tsung-Ting, Xiaoqian Jiang, Haixu Tang, et al.. (2022). The evolving privacy and security concerns for genomic data analysis and sharing as observed from the iDASH competition. Journal of the American Medical Informatics Association. 29(12). 2182–2190. 13 indexed citations

Baxter, Sally L., Bharanidharan Radha Saseendrakumar, Paulina Paul, et al.. (2021). Predictive Analytics for Glaucoma Using Data From the All of Us Research Program. American Journal of Ophthalmology. 227. 74–86. 38 indexed citations

10.

Kuo, Tsung-Ting, et al.. (2021). Previewable Contract-Based On-Chain X-Ray Image Sharing Framework for Clinical Research. International Journal of Medical Informatics. 156. 104599–104599. 10 indexed citations

11.

Kuo, Tsung-Ting, Tyler Bath, Nicholas D. Pattengale, et al.. (2021). Benchmarking blockchain-based gene-drug interaction data sharing methods: A case study from the iDASH 2019 secure genome analysis competition blockchain track. International Journal of Medical Informatics. 154. 104559–104559. 18 indexed citations

12.

Baxter, Sally L., Charles Marks, Tsung-Ting Kuo, Lucila Ohno‐Machado, & Robert N. Weinreb. (2019). Machine Learning-Based Predictive Modeling of Surgical Intervention in Glaucoma Using Systemic Data From Electronic Health Records. American Journal of Ophthalmology. 208. 30–40. 49 indexed citations

13.

Mackey, Tim K., Tsung-Ting Kuo, Kevin A. Clauson, et al.. (2019). ‘Fit-for-purpose?’ – challenges and opportunities for applications of blockchain technology in the future of healthcare. BMC Medicine. 17(1). 68–68. 187 indexed citations

14.

Gabriel, Rodney A., Tsung-Ting Kuo, Julian McAuley, & Chun‐Nan Hsu. (2018). Identifying and characterizing highly similar notes in big clinical note datasets. Journal of Biomedical Informatics. 82. 63–69. 15 indexed citations

15.

Gabriel, Rodney A., et al.. (2017). The presence of highly similar notes within the MIMIC-III dataset.. AMIA. 1 indexed citations

16.

Bhargava, Shitij, et al.. (2017). bioPDFX: preparing PDF scientific articles for biomedical text mining. 1 indexed citations

17.

Kashyap, Rahul, et al.. (2016). Weakly supervised learning of biomedical information extraction from curated data. BMC Bioinformatics. 17(S1). 1–1. 149 indexed citations

18.

Kuo, Tsung-Ting, et al.. (2012). Exploiting Latent Information to Predict Diffusions of Novel Topics on Social Networks. Meeting of the Association for Computational Linguistics. 344–348. 11 indexed citations

19.

Yu, Hsiang‐Fu, Hung-Yi Lo, Hsun-Ping Hsieh, et al.. (2010). Feature Engineering and Classifier Ensemble for KDD Cup 2010. Knowledge Discovery and Data Mining. 89 indexed citations

20.

Lo, Hung-Yi, Kai‐Wei Chang, Shang-Tse Chen, et al.. (2009). An ensemble of three classifiers for KDD cup 2009: expanded linear model, heterogeneous boosting, and selective naïve Bayes. Knowledge Discovery and Data Mining. 57–64. 11 indexed citations

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.

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