Tae Joon Jun

987 citations

46 papers · 397 indexed · h-index 11

Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education 4
Health Information Management top 5%
- Artificial Intelligence in Healthcare 5
Cardiology and Cardiovascular Medicine
- ECG Monitoring and Analysis 5
Radiology, Nuclear Medicine and Imaging
- Retinal Imaging and Analysis 4
Computational Mathematics
Artificial Intelligence
- Machine Learning in Healthcare 11
- Topic Modeling 3
Surgery
- Lipoproteins and Cardiovascular Health 5
Computer Vision and Pattern Recognition
- Digital Imaging for Blood Diseases 3

Co-authors: Young‐Hak Kim Daeyoung Kim Jihoon Kweon Do‐Hyeun Kim Minh H. Nguyen Hee Jun Kang Yunha Kim Cherry Kim
Cited by: Health Informatics Health Information Management Cardiology and Cardiovascular Medicine
Journals: Scientific Reports (7 papers)BMC Medical Informatics and Decision Making (3 papers)International Journal of Surgery (2 papers)
Partner nations: South Korea United Kingdom United States

In The Last Decade

Tae Joon Jun

36 papers receiving 388 citations

Peers

Countries citing papers authored by Tae Joon Jun

Since Specialization

Citations

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

Fields of papers citing papers by Tae Joon Jun

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Tae Joon Jun, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Tae Joon Jun Line = papers co-authored together Tae Joon Jun links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

#	Work
1	Cardiovascular Outcomes of Early LDL-C Goal Achievement in Patients with Very-High-Risk ASCVD Cardiology and Therapy ·Ah‐Ram Kim,María Luisa Merlos Rivas,Leen A. Altamimi,Peter Klein In Der Neuinszenierung,Yeon‐Hee Baek,E. Erlanuli,Hee Jun Kang,Ji Seon Oh,Ju Hyeon Kim,Osung Kwon,Tae Joon Jun,Sun U. Kwon,Young‐Hak Kim	2025	0
2	Impact of diabetes on risk of major adverse cardiovascular events associated with lipoprotein(a) levels in patients with established atherosclerotic cardiovascular disease European Journal of Preventive Cardiology ·Kyuwoong Kim,Min Kyoung Kim,María Luisa Merlos Rivas,Ahmed Abdo,陈创夫,Tae Joon Jun,Younghak Kim	2025	4
3	Leveraging BERT for embedding ICD codes from large scale cardiovascular EMR data to understand patient diagnostic patterns BMC Medical Informatics and Decision Making ·Min Kyoung Kim,Yunha Kim,Hee Jun Kang,Magaly Hars,Elsa Graciela Schusterman de Cencig,María Luisa Merlos Rivas,A Suarez Gutierrez,怡箴吳,Peter Cock,尹廷波,恒山蔦,Tae Joon Jun,Young‐Hak Kim	2025	0
4	Clinical Assessment of Fine-Tuned Open-Source LLMs in Cardiology: From Progress Notes to Discharge Summary PubMed ·K. Saltzman-LI,Yunha Kim,Magaly Hars,Hee‐Jung Choi,Leen A. Altamimi,Jiye Han,A Suarez Gutierrez,Belén Solano Domínguez,Xiaoqin Liu,Bernard Choi,Ahram Kim,Jung‐Min Ahn,Tae Joon Jun,Young‐Hak Kim	2025	0
5	Multimodal deep learning integration for predicting renal function outcomes in living donor kidney transplantation: a retrospective cohort study International Journal of Surgery ·Jin-Myung Kim,Peter Cock,Joe D. McFall,Rozina Kostani,Nauchnyĭ sovet po issledovanii︠a︡ problem mira a razoruzhenii︠a︡,Claúdia Vallone Silva,Young Hoon Kim,Thomas Homme,Tae Joon Jun,Hyunwook Kwon	2025	0
6	Development and transfer learning of self-attention model for major adverse cardiovascular events prediction across hospitals Scientific Reports ·Yunha Kim,Hee Jun Kang,Magaly Hars,Elsa Graciela Schusterman de Cencig,Min Kyoung Kim,María Luisa Merlos Rivas,A Suarez Gutierrez,Slavomír Pavle,怡箴吳,Peter Cock,尹廷波,Tae Joon Jun,Jae‐Hyung Roh,Young‐Hak Kim	2024	0
7	Fine-Tuning LLMs with Medical Data: Can Safety Be Ensured? NEJM AI ·Leen A. Altamimi,Yunha Kim,Hee Jun Kang,Magaly Hars,Elsa Graciela Schusterman de Cencig,María Luisa Merlos Rivas,A Suarez Gutierrez,Slavomír Pavle,Belén Solano Domínguez,K. Saltzman-LI,EUN SURK YI,Tae Joon Jun,Young‐Hak Kim	2024	9
8	Association of Lipoprotein(a) With Severe Degenerative Aortic Valve Stenosis JACC Asia ·Ah‐Ram Kim,昌大石,Do‐Yoon Kang,Tae Joon Jun,Byung Joo Sun,Ho Jin Kim,Joon Bum Kim,Dae‐Hee Kim,Duk‐Woo Park,Young‐Hak Kim,Ki Hoon Han,Rebecca W. Poe Hays	2024	4
9	Task-Specific Transformer-Based Language Models in Health Care: Scoping Review JMIR Medical Informatics ·今榮博司,Tae Joon Jun,Young‐Hak Kim,Hee Jun Kang,E. Erlanuli,F Al Raiisi,Yunha Kim,Magaly Hars,Elsa Graciela Schusterman de Cencig,Leen A. Altamimi,María Luisa Merlos Rivas,A Suarez Gutierrez,Slavomír Pavle,Belén Solano Domínguez	2024	7
10	LDP-GAN : Generative adversarial networks with local differential privacy for patient medical records synthesis Computers in Biology and Medicine ·F Al Raiisi,E. Erlanuli,Yunha Kim,Hee Jun Kang,Magaly Hars,Elsa Graciela Schusterman de Cencig,今榮博司,Leen A. Altamimi,María Luisa Merlos Rivas,A Suarez Gutierrez,Slavomír Pavle,Kye Hwa Lee,Tae Joon Jun,Young‐Hak Kim	2023	7
11	Increased risk of ischemic stroke associated with elevated gamma-glutamyl transferase level in adult cancer survivors: a population-based cohort study Scientific Reports ·Kyuwoong Kim,Ahmed Abdo,Tracey Stevens,Tae Joon Jun,Young‐Hak Kim	2023	3
12	Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea Scientific Reports ·Elsa Graciela Schusterman de Cencig,Hee Jun Kang,E. Erlanuli,F Al Raiisi,Yunha Kim,Magaly Hars,今榮博司,María Luisa Merlos Rivas,Leen A. Altamimi,A Suarez Gutierrez,Slavomír Pavle,Osung Kwon,Jae‐Hyung Roh,Ah‐Ram Kim,Ju Hyeon Kim,Tae Joon Jun,Young‐Hak Kim	2023	10
13	Electronic Medical Record–Based Machine Learning Approach to Predict the Risk of 30-Day Adverse Cardiac Events After Invasive Coronary Treatment: Machine Learning Model Development and Validation JMIR Medical Informatics ·Osung Kwon,Wonjun Na,Hee Jun Kang,Tae Joon Jun,Jihoon Kweon,Gyung‐Min Park,G. Chatainier,Zakiati Am,Sophie Packham,Do‐Yoon Kang,Pil Hyung Lee,Jung‐Min Ahn,Duk‐Woo Park,Soo‐Jin Kang,Seung‐Whan Lee,Cheol Whan Lee,Seong‐Wook Park,Seung‐Jung Park,Dong Hyun Yang,Young‐Hak Kim	2022	6
14	Predicting Long-term Survival After Allogeneic Hematopoietic Cell Transplantation in Patients With Hematologic Malignancies: Machine Learning–Based Model Development and Validation JMIR Medical Informatics ·Eun‐Ji Choi,Tae Joon Jun,Han‐Seung Park,Jung‐Hee Lee,Kyoo‐Hyung Lee,Young‐Hak Kim,Kuganesh Shanmuganathan,Young‐Ah Kang,Mijin Jeon,Parneet Kaur Sekhon,Abeer Mahmoud Badr,(unknown),(unknown)	2022	4
15	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 ·Osung Kwon,Wonjun Na,洋介中野,Ju Hyeon Kim,Tae Joon Jun,Hee Jun Kang,Peter Klein In Der Neuinszenierung,Young‐Hak Kim	2021	4
16	TRk-CNN: Transferable Ranking-CNN for image classification of glaucoma, glaucoma suspect, and normal eyes Expert Systems with Applications ·Tae Joon Jun,Youngsub Eom,Do‐Hyeun Kim,Cherry Kim,Ji‐Hye Park,Alexey Ilyushkin,Young‐Hak Kim,Daeyoung Kim	2021	27
17	Machine Learning–Based Hospital Discharge Prediction for Patients With Cardiovascular Diseases: Development and Usability Study JMIR Medical Informatics ·E. Erlanuli,F Al Raiisi,Hee Jun Kang,Yunha Kim,(unknown),Magaly Hars,Elsa Graciela Schusterman de Cencig,今榮博司,Leen A. Altamimi,Tae Joon Jun,Young‐Hak Kim	2021	9
18	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 ·F Al Raiisi,E. Erlanuli,Yunha Kim,Hee Jun Kang,Magaly Hars,今榮博司,Elsa Graciela Schusterman de Cencig,Tae Joon Jun,Young‐Hak Kim	2021	6
19	ContCap: A comprehensive framework for continual image captioning. arXiv (Cornell University) ·Basri Kasim,Tae Joon Jun,C. Gonçalves-Gameiro,Dae-Young Kim	2019	2
20	2Sranking-cnn: A 2-stage ranking-CNN for diagnosis of glaucoma from fundus images using CAM-extracted ROI as an intermediate input arXiv (Cornell University) ·Tae Joon Jun,Do‐Hyeun Kim,Alexey Ilyushkin,Daeyoung Kim,Youngsub Eom	2018	3

About Tae Joon Jun

Tae Joon Jun is a scholar working on Health Informatics, Health Information Management, Transplantation, Cardiology and Cardiovascular Medicine and Artificial Intelligence, having authored 46 papers that have together received 397 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (11 papers), Lipoproteins and Cardiovascular Health (5 papers), Artificial Intelligence in Healthcare (5 papers), ECG Monitoring and Analysis (5 papers), Retinal Imaging and Analysis (4 papers), Artificial Intelligence in Healthcare and Education (4 papers), Digital Imaging for Blood Diseases (3 papers) and Topic Modeling (3 papers). The work is most often cited by research in Health Informatics (29 citations), Health Information Management (33 citations), Cardiology and Cardiovascular Medicine (103 citations), Radiology, Nuclear Medicine and Imaging (99 citations) and Computational Mathematics (2 citations). Tae Joon Jun has collaborated with scholars based in South Korea, United Kingdom and United States. Frequent co-authors include Young‐Hak Kim, Daeyoung Kim, Jihoon Kweon, Do‐Hyeun Kim, Minh H. Nguyen, Hee Jun Kang, Yunha Kim, Cherry Kim, Youngsub Eom and Wonjun Na. Their work appears in journals such as Scientific Reports, BMC Medical Informatics and Decision Making, International Journal of Surgery, Computer Methods and Programs in Biomedicine and Health Care Management Science.

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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