Eric Perakslis

36 papers receiving 800 citations

Peers

Eric Perakslis
Comparison fields: 5 of 133
  • Health Informatics 64
  • Applied Psychology 85
  • Health Information Management 62
  • Family Practice 26
  • General Health Professions 152
Replace Yu Rang Park with:
Yu Rang Park South Korea
François Modave United States
Sharareh Rostam Niakan Kalhori Iran
Sanjeev P. Bhavnani United States
Niloofar Mohammadzadeh Iran
Carlos Luís Parra-Calderón Spain
Taridzo Chomutare Norway
Marjan Ghazisaeedi Iran
Miguel Ángel Mayer Spain
Andrea Coravos United States
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Citations per year

Countries citing papers authored by Eric Perakslis

Since Specialization
Citations

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

Fields of papers citing papers by Eric Perakslis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 38 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017158
2 201999
3 201884
4 201063
5 201458
6 202342
7 202042
8 202037
9 201929
10 201828
11 201927
12 202126
13 201017
14 201715
15 201613
16 202213
17 199712
18 201910
19 20077
20 20226

About Eric Perakslis

Eric Perakslis is a scholar working on Health Informatics, Applied Psychology, Family Practice, Health Information Management and Transplantation, having authored 38 papers that have together received 835 indexed citations. Recurring topics across this work include Ethics in Clinical Research (7 papers), Digital Mental Health Interventions (5 papers), Information and Cyber Security (4 papers), Mobile Health and mHealth Applications (3 papers), Big Data and Business Intelligence (3 papers), Artificial Intelligence in Healthcare and Education (3 papers), Biomedical Text Mining and Ontologies (3 papers) and Biomedical and Engineering Education (3 papers). The work is most often cited by research in Health Informatics (64 citations), Applied Psychology (85 citations), Health Information Management (62 citations), Family Practice (26 citations) and General Health Professions (152 citations). Eric Perakslis has collaborated with scholars based in United States, Japan and Canada. Frequent co-authors include John A. Wagner, Elena S. Izmailova, Geoffrey S. Ginsburg, Sándor Szalma, Andrea Coravos, Mohammad S. Jalali, Stuart Madnick, William J. Gordon, Venkata Koka and Jennifer C. Goldsack. Their work appears in journals such as New England Journal of Medicine, JAMA, Clinical Pharmacology & Therapeutics, Nature Reviews Drug Discovery and Journal of Medical Internet Research.

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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