Won Chul

3.6k citations
163 papers · 2.3k indexed · h-index 27

Impact in

    • Cardiac Arrest and Resuscitation
    • Emergency and Acute Care Studies
    • Trauma and Emergency Care Studies
    • Artificial Intelligence in Healthcare and Education

Papers in

Won Chul

152 papers receiving 2.2k citations

Peers

Won Chul
Comparison fields: 5 of 142
  • Emergency Medicine 944
  • Health Informatics 105
  • Critical Care and Intensive Care Medicine 177
  • Family Practice 75
  • Health Information Management 115
Replace Tadahiro Goto with:
Tadahiro Goto Japan
Abhishek Deshmukh United States
Romain Pirracchio France
Vincent Liu United States
Michael W. Sjoding United States
Trevor C. Yuen United States
Jeremiah S. Hinson United States
Brian W. Pickering United States
Omar Badawi United States
Kathryn H. Bowles United States
Won Chul relative to Tadahiro Goto Japan Tadahiro Goto's profile →
Citations per field
00.5×1.5×2.0×
Tadahiro Goto · 1×
Citations per year

Countries citing papers authored by Won Chul

Since Specialization
Citations

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

Fields of papers citing papers by Won Chul

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Won Chul, 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 Won Chul Line = papers co-authored together Won Chul links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20253
2 20240
3 20249
4 20242
5 20240
6 20234
7 202258
8 20221
9 20221
10 20212
11 202127
12 202010
13 202029
14 20195
15 20196
16 20194
17 201825
18 20187
19 201813
20
A Survey of Emergency Department Health Care Provider on Awareness of Elder Abuse
20163

About Won Chul

Won Chul is a scholar working on Emergency Medicine, Health Informatics, Emergency Medical Services, Health Information Management and Family Practice, having authored 163 papers that have together received 2.3k indexed citations. Recurring topics across this work include Cardiac Arrest and Resuscitation (40 papers), Emergency and Acute Care Studies (37 papers), Trauma and Emergency Care Studies (34 papers), Sepsis Diagnosis and Treatment (31 papers), Disaster Response and Management (11 papers), Electronic Health Records Systems (11 papers), Machine Learning in Healthcare (11 papers) and Hemodynamic Monitoring and Therapy (11 papers). The work is most often cited by research in Emergency Medicine (944 citations), Health Informatics (105 citations), Critical Care and Intensive Care Medicine (177 citations), Family Practice (75 citations) and Health Information Management (115 citations). Won Chul has collaborated with scholars based in South Korea, United States and Singapore. Frequent co-authors include Ik Joon Jo, Tae Gun Shin, Sang Do Shin, Min Seob Sim, Sung Yeon Hwang, Hee Yoon, Kyoung Jun Song, Tae Rim Lee, Eui Jung Lee and Ki Ok Ahn. Their work appears in journals such as Journal of Korean Medical Science, Scientific Reports, JMIR mhealth and uhealth, The American Journal of Emergency Medicine and Resuscitation.

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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