Dukyong Yoon

2.1k total citations
99 papers, 1.4k citations indexed

About

Dukyong Yoon is a scholar working on Cardiology and Cardiovascular Medicine, Pulmonary and Respiratory Medicine and Surgery. According to data from OpenAlex, Dukyong Yoon has authored 99 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Cardiology and Cardiovascular Medicine, 23 papers in Pulmonary and Respiratory Medicine and 15 papers in Surgery. Recurrent topics in Dukyong Yoon's work include ECG Monitoring and Analysis (14 papers), Cardiac electrophysiology and arrhythmias (11 papers) and Electronic Health Records Systems (7 papers). Dukyong Yoon is often cited by papers focused on ECG Monitoring and Analysis (14 papers), Cardiac electrophysiology and arrhythmias (11 papers) and Electronic Health Records Systems (7 papers). Dukyong Yoon collaborates with scholars based in South Korea, United States and United Kingdom. Dukyong Yoon's co-authors include Rae Woong Park, Tae Young Kim, Hong‐Seok Lim, Young Choi, Man Young Park, Young‐Gun Kim, Sukhoon Lee, Soo Yeon Cho, Kyoungwon Jung and Hyoun‐Ah Kim and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Dukyong Yoon

93 papers receiving 1.3k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Dukyong Yoon South Korea 22 310 216 210 158 136 99 1.4k
Tahmina Nasrin Poly Taiwan 25 147 0.5× 360 1.7× 177 0.8× 275 1.7× 122 0.9× 59 2.0k
Liqin Wang United States 24 97 0.3× 142 0.7× 109 0.5× 76 0.5× 324 2.4× 84 1.5k
Emmanuel Chazard France 19 117 0.4× 176 0.8× 68 0.3× 172 1.1× 127 0.9× 127 1.1k
Preciosa M. Coloma Netherlands 25 163 0.5× 196 0.9× 125 0.6× 109 0.7× 216 1.6× 46 1.9k
Alexander Michel Germany 12 425 1.4× 78 0.4× 137 0.7× 115 0.7× 90 0.7× 29 1.2k
Seng Chan You South Korea 23 649 2.1× 331 1.5× 205 1.0× 97 0.6× 146 1.1× 125 2.0k
Jenna Reps United States 16 282 0.9× 86 0.4× 103 0.5× 478 3.0× 119 0.9× 61 1.7k
Lilly Q. Yue United States 17 137 0.4× 230 1.1× 188 0.9× 47 0.3× 262 1.9× 43 2.3k
Hsuan‐Chia Yang Taiwan 27 164 0.5× 377 1.7× 216 1.0× 308 1.9× 195 1.4× 100 2.4k
Mark de Groot Netherlands 24 263 0.8× 350 1.6× 236 1.1× 29 0.2× 245 1.8× 81 1.6k

Countries citing papers authored by Dukyong Yoon

Since Specialization
Citations

This map shows the geographic impact of Dukyong Yoon'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 Dukyong Yoon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dukyong Yoon more than expected).

Fields of papers citing papers by Dukyong Yoon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dukyong Yoon. 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 Dukyong Yoon. The network helps show where Dukyong Yoon may publish in the future.

Co-authorship network of co-authors of Dukyong Yoon

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

All Works

20 of 20 papers shown
1.
Jung, Yun Jung, et al.. (2024). Artificial Intelligence-Based Early Prediction of Acute Respiratory Failure in the Emergency Department Using Biosignal and Clinical Data. Yonsei Medical Journal. 66(2). 121–121. 1 indexed citations
2.
Kim, Jae-Bong, Yujeong Kim, Soo‐Jeong Kim, et al.. (2024). Integration of National Health Insurance claims data and animal models reveals fexofenadine as a promising repurposed drug for Parkinson’s disease. Journal of Neuroinflammation. 21(1). 53–53. 1 indexed citations
3.
You, Seng Chan, et al.. (2024). Evaluation of GPT-4 for 10-year cardiovascular risk prediction: Insights from the UK Biobank and KoGES data. iScience. 27(2). 109022–109022. 12 indexed citations
4.
Chung, Wou Young, et al.. (2024). Development and Validation of Deep Learning–Based Infectivity Prediction in Pulmonary Tuberculosis Through Chest Radiography: Retrospective Study. Journal of Medical Internet Research. 26. e58413–e58413. 1 indexed citations
5.
Jeong, Kyunguk, et al.. (2024). Investigation of the prevalence and treatment of atopic dermatitis in South Korea using a large national dataset. Korean Journal of Family Medicine. 46(6). 426–434.
6.
7.
Lee, Deokjong, et al.. (2023). Changes in the Circadian Rhythm of High-Frequency Heart Rate Variability Associated With Depression. Journal of Korean Medical Science. 38(19). e142–e142. 4 indexed citations
8.
Son, Nak‐Hoon, Kyoung Min Kim, Dukyong Yoon, et al.. (2022). Establishment of muscle mass-based indications for the cystatin C test in renal function evaluation. Frontiers in Medicine. 9. 1021936–1021936. 5 indexed citations
9.
Kang, Sora, Chul Park, Jinseok Lee, & Dukyong Yoon. (2022). Machine Learning Model for the Prediction of Hemorrhage in Intensive Care Units. Healthcare Informatics Research. 28(4). 364–375. 6 indexed citations
10.
Jung, Ju‐Yang, Hyun Young Lee, Eunyoung Lee, et al.. (2022). Three Clinical Clusters Identified through Hierarchical Cluster Analysis Using Initial Laboratory Findings in Korean Patients with Systemic Lupus Erythematosus. Journal of Clinical Medicine. 11(9). 2406–2406. 3 indexed citations
11.
Kim, Tae Young, et al.. (2021). Effectiveness of Transfer Learning for Deep Learning-Based Electrocardiogram Analysis. Healthcare Informatics Research. 27(1). 19–28. 33 indexed citations
12.
Kim, Yujeong, et al.. (2021). Machine Learning Approach for Active Vaccine Safety Monitoring. Journal of Korean Medical Science. 36(31). e198–e198. 6 indexed citations
13.
14.
Yoon, Dukyong, et al.. (2020). Discovering hidden information in biosignals from patients using artificial intelligence. Korean journal of anesthesiology. 73(4). 275–284. 22 indexed citations
15.
Roh, Hyun Woong, Sang Joon Son, Chang Hyung Hong, et al.. (2020). Deep Learning Approach for Imputation of Missing Values in Actigraphy Data: Algorithm Development Study. JMIR mhealth and uhealth. 8(7). e16113–e16113. 22 indexed citations
16.
Jung, Ju‐Yang, Dukyong Yoon, Young Hwa Choi, Hyoun‐Ah Kim, & Chang‐Hee Suh. (2019). Associated clinical factors for serious infections in patients with systemic lupus erythematosus. Scientific Reports. 9(1). 9704–9704. 35 indexed citations
17.
Yoon, Dukyong, et al.. (2018). Quantitative Evaluation of the Relationship between T-Wave-Based Features and Serum Potassium Level in Real-World Clinical Practice. BioMed Research International. 2018. 1–7. 11 indexed citations
18.
Jung, Ju‐Yang, Young Choi, Chang‐Hee Suh, Dukyong Yoon, & Hyoun‐Ah Kim. (2018). Effect of fenofibrate on uric acid level in patients with gout. Scientific Reports. 8(1). 16767–16767. 25 indexed citations
19.
Yim, Shin-Young, Dukyong Yoon, Myong Chul Park, et al.. (2013). Integrative analysis of congenital muscular torticollis: from gene expression to clinical significance. BMC Medical Genomics. 6(S2). S10–S10. 16 indexed citations
20.
Yoon, Dukyong, et al.. (2012). Construction of an Open-Access QT Database for Detecting the Proarrhythmia Potential of Marketed Drugs: ECG-ViEW. Clinical Pharmacology & Therapeutics. 92(3). 393–396. 16 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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