Scott Kaplin

15 papers receiving 485 citations

Scott Kaplin's Hit Papers

Machine learning prediction in cardiovascular diseases: a meta-analysis 2020 · 264 citations
2640+2+4Years since publication50100150200250

Peers

Scott Kaplin
Comparison fields: 5 of 122
  • Health Informatics 48
  • Health Information Management 136
  • Medical Laboratory Technology 12
  • Cardiology and Cardiovascular Medicine 143
  • Aging 8
Replace Fatma Hilal Yağın with:
Fatma Hilal Yağın Türkiye
Yikuan Li United Kingdom
Konstantia Zarkogianni Greece
Joseph El Youssef United States
Leah M. Wilson United States
Wan‐Tai M. Au‐Yeung United States
Gema García-Sáez Spain
Monika Reddy United Kingdom
Worrawat Engchuan Canada
Nino Isakadze United States
Scott Kaplin relative to Fatma Hilal Yağın Türkiye Fatma Hilal Yağın's profile →
Citations per field
00.5×10.8×
Fatma Hilal Yağın · 1×
Citations per year

Countries citing papers authored by Scott Kaplin

Since Specialization
Citations

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

Fields of papers citing papers by Scott Kaplin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

17 of 17 papers shown
#Work
1
Machine learning prediction in cardiovascular diseases: a meta-analysis
Hit paper breakdown →
2020264
2 202262
3 202143
4 202232
5 202228
6 202121
7 202315
8 201911
9 20218
10 20217
11 20216
12 20216
13 20193
14 20193
15 20211
16 20210
17 20170

About Scott Kaplin

Scott Kaplin is a scholar working on Cardiology and Cardiovascular Medicine, Surgery, Pulmonary and Respiratory Medicine, Health, Toxicology and Mutagenesis and Physiology, having authored 17 papers that have together received 510 indexed citations. Recurring topics across this work include Spaceflight effects on biology (2 papers), Acute Myocardial Infarction Research (2 papers), Climate Change and Health Impacts (2 papers), Phytochemicals and Medicinal Plants (1 paper), Bioactive Natural Diterpenoids Research (1 paper), Space Exploration and Technology (1 paper), COVID-19 diagnosis using AI (1 paper) and Nutritional Studies and Diet (1 paper). The work is most often cited by research in Health Informatics (48 citations), Health Information Management (136 citations), Medical Laboratory Technology (12 citations), Cardiology and Cardiovascular Medicine (143 citations) and Aging (8 citations). Scott Kaplin has collaborated with scholars based in United States, Belarus and Switzerland. Frequent co-authors include Chayakrit Krittanawong, Zhen Wang, Hafeez Ul Hassan Virk, W.H. Wilson Tang, Bharat Narasimhan, Kipp W. Johnson, Jonathan L. Halperin, Usman Baber, HongJu Zhang and Sripal Bangalore. Their work appears in journals such as The American Journal of Medicine, The American Journal of Cardiology, Progress in Cardiovascular Diseases, Scientific Reports and Cells.

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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