Joo Heung Yoon

1.1k citations
27 papers · 664 indexed · h-index 12
Topics
Machine Learning in Healthcare (4 papers)Artificial Intelligence in Healthcare and Education (3 papers)Coronary Interventions and Diagnostics (3 papers)

In The Last Decade

Joo Heung Yoon

25 papers receiving 651 citations

Peers

Joo Heung Yoon
Comparison fields: 5 of 113
  • Surgery 222
  • Pulmonary and Respiratory Medicine 133
  • Cardiology and Cardiovascular Medicine 123
  • Radiology, Nuclear Medicine and Imaging 121
  • Biomedical Engineering 115
Replace Sang Eun Lee with:
Sang Eun Lee South Korea
Thomas Jack Germany
Andrew T. Strong United States
Pattharawin Pattharanitima Thailand
David Snipelisky United States
Murtadha Al‐Khabori Oman
Ahmet İlker Tekkeşin Türkiye
David Liew Australia
Muhammad Naeem Pakistan
Andrew W. Shih Canada
Joo Heung Yoon relative to Sang Eun Lee South Korea Sang Eun Lee's profile →
Citations per field
00.5×1.5×
Sang Eun Lee · 1×
Citations per year

Countries citing papers authored by Joo Heung Yoon

Since Specialization
Citations

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

Fields of papers citing papers by Joo Heung Yoon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Joo Heung Yoon

This figure shows the co-authorship network connecting the top 25 collaborators of Joo Heung Yoon. A scholar is included among the top collaborators of Joo Heung 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 Joo Heung Yoon. Joo Heung 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
#WorkIndexed citations
1 7
2 2
3 70
4 12
5 6
6 7
7 30
8 25
9 16
10 57
11 80
12 91
13 120
14 2
15 9
16 8
17 9
18 41
19 4
20 9

About Joo Heung Yoon

Joo Heung Yoon is a scholar working on Health Informatics, Transplantation and Critical Care and Intensive Care Medicine, having authored 27 papers that have together received 664 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (4 papers), Artificial Intelligence in Healthcare and Education (3 papers) and Coronary Interventions and Diagnostics (3 papers). The work is most often cited by research in Health Informatics (63 citations), Transplantation (28 citations) and Critical Care and Intensive Care Medicine (32 citations). Joo Heung Yoon has collaborated with scholars based in United States, South Korea and Italy. Frequent co-authors include Michael R. Pinsky, Gilles Clermont, Ik–Kyung Jang, Qingen Ke, Donghyun Hong, Soochan Bae, Gilson Khang, Peter M. Kang, Hyungsuk Lim and Dongwon Lee. Their work appears in journals such as PLoS ONE, Scientific Reports and The American Journal of Cardiology.

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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