Leon Kopitar

404 citations
5 papers · 238 · 1 hit paper · h-index 4

Impact in

Papers in

Leon Kopitar

5 papers receiving 228 citations

Leon Kopitar's Hit Papers

Early detection of type 2 diabetes mellitus using machine learning-based prediction models 2020 · 227 citations
2270+2+4Years since publication50100150200

Peers

Leon Kopitar
Comparison fields: 5 of 75
  • Health Information Management 160
  • Health Informatics 13
  • Artificial Intelligence 136
  • Complementary and alternative medicine 21
  • Endocrinology, Diabetes and Metabolism 39
Replace Shahid Mohammad Ganie with:
Shahid Mohammad Ganie India
Tarun Gangil India
Dola Das Bangladesh
Paolo Misericordia Italy
Ashok Kumar Dwivedi India
Dehui Yin China
Mathieu Ravaut Singapore
Aishwariya Dutta Bangladesh
Aixia Guo United States
Won-Suk Oh United States
Leon Kopitar relative to Shahid Mohammad Ganie India Shahid Mohammad Ganie's profile →
Citations per field
00.5×5.1×
Shahid Mohammad Ganie · 1×
Citations per year

Countries citing papers authored by Leon Kopitar

Since Specialization
Citations

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

Fields of papers citing papers by Leon Kopitar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 8 scholars most cited alongside Leon Kopitar, 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 Leon Kopitar Line = papers co-authored together Leon Kopitar links everyone, so they are left out of the graph.

All Works

About Leon Kopitar

Leon Kopitar is a scholar working on Health Information Management, Artificial Intelligence, Health, Cardiology and Cardiovascular Medicine and Signal Processing, having authored 5 papers that have together received 238 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare (2 papers), Machine Learning in Healthcare (2 papers), Cardiovascular Health and Risk Factors (1 paper), Mental Health Research Topics (1 paper), Health disparities and outcomes (1 paper), Time Series Analysis and Forecasting (1 paper), Diet and metabolism studies (1 paper) and Traditional Chinese Medicine Studies (1 paper). The work is most often cited by research in Health Information Management (160 citations), Health Informatics (13 citations), Artificial Intelligence (136 citations), Complementary and alternative medicine (21 citations) and Endocrinology, Diabetes and Metabolism (39 citations). Leon Kopitar has collaborated with scholars based in Slovenia, United Kingdom and United States. Frequent co-authors include Gregor Štiglic, Primož Kocbek, Aziz Sheikh, Leona Cilar, Larissa J. Strath, Iztok Fister, Peter Kokol and Jiang Bian. Their work appears in journals such as Nutrients, Scientific Reports, Journal of Biomedical Informatics and Information.

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.

Explore authors with similar magnitude of impact