Hee Jun Kang

1.2k total citations
48 papers, 555 citations indexed

About

Hee Jun Kang is a scholar working on Artificial Intelligence, Cardiology and Cardiovascular Medicine and Surgery. According to data from OpenAlex, Hee Jun Kang has authored 48 papers receiving a total of 555 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 9 papers in Cardiology and Cardiovascular Medicine and 7 papers in Surgery. Recurrent topics in Hee Jun Kang's work include Machine Learning in Healthcare (8 papers), Cardiac Imaging and Diagnostics (5 papers) and Artificial Intelligence in Healthcare (4 papers). Hee Jun Kang is often cited by papers focused on Machine Learning in Healthcare (8 papers), Cardiac Imaging and Diagnostics (5 papers) and Artificial Intelligence in Healthcare (4 papers). Hee Jun Kang collaborates with scholars based in South Korea, United States and China. Hee Jun Kang's co-authors include Cheng-Xian Lin, M. A. Ebadian, Young‐Hak Kim, Dong Hyun Yang, Duy-Tang Hoang, Tae Joon Jun, June‐Goo Lee, Sang‐Jong Park, Yunha Kim and Junsang Moon and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and International Journal of Heat and Mass Transfer.

In The Last Decade

Hee Jun Kang

37 papers receiving 545 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hee Jun Kang South Korea 12 150 109 102 85 60 48 555
Ali Mottaghi United States 4 229 1.5× 92 0.8× 18 0.2× 42 0.5× 40 0.7× 5 743
Xiaoyong Wang China 13 81 0.5× 36 0.3× 44 0.4× 49 0.6× 62 1.0× 65 587
Zbigniew Nawrat Poland 11 71 0.5× 240 2.2× 59 0.6× 136 1.6× 13 0.2× 60 542
Shan Hu China 15 215 1.4× 68 0.6× 99 1.0× 154 1.8× 31 0.5× 63 865
Harshawn Malhi United States 10 196 1.3× 67 0.6× 13 0.1× 96 1.1× 39 0.7× 14 439
Jichen Yang United States 6 155 1.0× 70 0.6× 13 0.1× 41 0.5× 26 0.4× 19 481
Mohammed Yusuf Ansari Qatar 13 251 1.7× 162 1.5× 28 0.3× 43 0.5× 36 0.6× 21 696
Xinglong Wu China 8 338 2.3× 80 0.7× 12 0.1× 72 0.8× 37 0.6× 37 703

Countries citing papers authored by Hee Jun Kang

Since Specialization
Citations

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

Fields of papers citing papers by Hee Jun Kang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hee Jun Kang. 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 Hee Jun Kang. The network helps show where Hee Jun Kang may publish in the future.

Co-authorship network of co-authors of Hee Jun Kang

This figure shows the co-authorship network connecting the top 25 collaborators of Hee Jun Kang. A scholar is included among the top collaborators of Hee Jun Kang 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 Hee Jun Kang. Hee Jun Kang 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.
Kim, Ah‐Ram, Yeon‐Hee Baek, Hee Jun Kang, et al.. (2025). Cardiovascular Outcomes of Early LDL-C Goal Achievement in Patients with Very-High-Risk ASCVD. Cardiology and Therapy. 14(1). 101–115.
2.
Kim, Min Kyoung, et al.. (2025). Leveraging BERT for embedding ICD codes from large scale cardiovascular EMR data to understand patient diagnostic patterns. BMC Medical Informatics and Decision Making. 25(1). 300–300.
3.
Tian, Bin, Hee Jun Kang, Xiaoyun Chen, et al.. (2025). Application and prospects of genetic engineering in CAR-NK cell therapy. Frontiers in Immunology. 16. 1600411–1600411. 2 indexed citations
4.
Kim, Yunha, Hee Jun Kang, Min Kyoung Kim, et al.. (2024). Development and transfer learning of self-attention model for major adverse cardiovascular events prediction across hospitals. Scientific Reports. 14(1). 23443–23443.
5.
Kim, Yunha, et al.. (2024). Fine-Tuning LLMs with Medical Data: Can Safety Be Ensured?. NEJM AI. 2(1). 9 indexed citations
6.
Jun, Tae Joon, et al.. (2024). Task-Specific Transformer-Based Language Models in Health Care: Scoping Review. JMIR Medical Informatics. 12. e49724–e49724. 7 indexed citations
7.
Kang, Hee Jun, et al.. (2024). Improvement of Torque according to the Movement Path of a Four-axis Multi-joint Robot. Journal of the Korean Society of Manufacturing Process Engineers. 23(10). 30–39.
8.
Kim, Yunha, et al.. (2023). LDP-GAN : Generative adversarial networks with local differential privacy for patient medical records synthesis. Computers in Biology and Medicine. 168. 107738–107738. 7 indexed citations
9.
Kang, Hee Jun, Yunha Kim, Osung Kwon, et al.. (2023). Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea. Scientific Reports. 13(1). 22461–22461. 10 indexed citations
10.
11.
Suh, Young Joo, Cherry Kim, June‐Goo Lee, et al.. (2022). Fully automatic coronary calcium scoring in non-ECG-gated low-dose chest CT: comparison with ECG-gated cardiac CT. European Radiology. 33(2). 1254–1265. 15 indexed citations
12.
Lee, June‐Goo, Heesoo Kim, Hee Jun Kang, et al.. (2021). Fully Automatic Coronary Calcium Score Software Empowered by Artificial Intelligence Technology: Validation Study Using Three CT Cohorts. Korean Journal of Radiology. 22(11). 1764–1764. 40 indexed citations
13.
Kwon, Osung, Wonjun Na, Ju Hyeon Kim, et al.. (2021). Cardiovascular Event Rates in Statin-Treated Korean Patients with Cardiovascular Disease: Estimates from a Real-World Population Using Electronic Medical Record Data. Cardiovascular Drugs and Therapy. 37(1). 129–140. 4 indexed citations
14.
Kang, Hee Jun, et al.. (2021). Machine Learning–Based Hospital Discharge Prediction for Patients With Cardiovascular Diseases: Development and Usability Study. JMIR Medical Informatics. 9(11). e32662–e32662. 9 indexed citations
15.
Kim, Yunha, et al.. (2021). Self–Training With Quantile Errors for Multivariate Missing Data Imputation for Regression Problems in Electronic Medical Records: Algorithm Development Study. JMIR Public Health and Surveillance. 7(10). e30824–e30824. 6 indexed citations
16.
Choe, Jooae, Hyun Jung Koo, Joon‐Won Kang, et al.. (2021). Aortic annulus sizing in bicuspid and tricuspid aortic valves using CT in patients with surgical aortic valve replacement. Scientific Reports. 11(1). 21005–21005. 4 indexed citations
17.
Hoang, Duy-Tang & Hee Jun Kang. (2018). Deep Belief Network and Dempster-Shafer Evidence Theory for Bearing Fault Diagnosis. 841–846. 16 indexed citations
18.
Lee, Heung-Man, et al.. (2009). Effect of Peroxisome Proliferator—Activated Receptor Gamma Agonists on Myofibroblast Differentiation and Collagen Production in Nasal Polyp—Derived Fibroblasts. Annals of Otology Rhinology & Laryngology. 118(10). 721–727. 10 indexed citations
19.
Kang, Hee Jun, et al.. (2004). State estimation for autonomous guided vehicle using the extended Kalman filter. Asian Control Conference. 1. 405–411. 2 indexed citations
20.
Son, Byung Ho, Sung Hee Hong, Hee Jun Kang, Ho Sung Yoon, & Sei Hyun Ahn. (2003). Pattern of Secondary Failure and Prognostic Factors for Survival Following Surgical Treatment of Isolated Locoregional Recurrence after Mastectomy of Breast Cancer. Journal of the Korean Surgical Society. 64(4). 282–288.

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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