Gina Barnes

888 total citations
31 papers, 486 citations indexed

About

Gina Barnes is a scholar working on Epidemiology, Artificial Intelligence and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Gina Barnes has authored 31 papers receiving a total of 486 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Epidemiology, 6 papers in Artificial Intelligence and 5 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Gina Barnes's work include Sepsis Diagnosis and Treatment (5 papers), Autism Spectrum Disorder Research (5 papers) and Machine Learning in Healthcare (4 papers). Gina Barnes is often cited by papers focused on Sepsis Diagnosis and Treatment (5 papers), Autism Spectrum Disorder Research (5 papers) and Machine Learning in Healthcare (4 papers). Gina Barnes collaborates with scholars based in United States, United Kingdom and Belgium. Gina Barnes's co-authors include Ritankar Das, Jacob Calvert, Qingqing Mao, Jana Hoffman, Anna Siefkas, Emily Pellegrini, Abigail Green‐Saxena, Samson Mataraso, Anurag Garikipati and Hoyt Burdick and has published in prestigious journals such as Circulation, Scientific Reports and Critical Care Medicine.

In The Last Decade

Gina Barnes

28 papers receiving 444 citations

Peers

Gina Barnes
Comparison fields: 5 of 116
  • Artificial Intelligence 129
  • Radiology, Nuclear Medicine and Imaging 109
  • Epidemiology 104
  • Health Informatics 78
  • Health Information Management 51
Replace Anna Siefkas with:
Anna Siefkas United States
Samson Mataraso United States
Emily Pellegrini United States
Shorabuddin Syed United States
Marshall Nichols United States
JoonNyung Heo South Korea
Chenxi Huang United States
Nancy Gentry United States
Albert Buchard United Kingdom
Hendrikus J. A. van Os Netherlands
Anna Siefkas United States View profile →
Citations per field, relative to Gina Barnes
Gina Barnes · 1×
Citations per year, relative to Gina Barnes
Gina Barnes · 1×

Countries citing papers authored by Gina Barnes

Since Specialization
Citations

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

Fields of papers citing papers by Gina Barnes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gina Barnes

This figure shows the co-authorship network connecting the top 25 collaborators of Gina Barnes. A scholar is included among the top collaborators of Gina Barnes 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 Gina Barnes. Gina Barnes 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
# Work Indexed citations
1 0
2 0
3 1
4 33
5 7
6 4
7 6
8 12
9 22
10 3
11
Using Machine Learning as a Precision Medicine Approach for Remdesivir and Corticosteroids as COVID-19 Pharmacotherapies
1
12 24
13 15
14 11
15 7
16 26
17 35
18 96
19 2
20 9

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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