German Prado-Arechiga

729 citations
24 papers · 346 indexed · h-index 9
Topics
Fuzzy Logic and Control Systems (7 papers)Neural Networks and Applications (4 papers)Artificial Intelligence in Healthcare (3 papers)
Partner nations
MexicoChina

In The Last Decade

German Prado-Arechiga

23 papers receiving 324 citations

Peers

German Prado-Arechiga
Comparison fields: 5 of 73
  • Artificial Intelligence 199
  • Cardiology and Cardiovascular Medicine 72
  • Health Information Management 49
  • Cognitive Neuroscience 45
  • Biomedical Engineering 39
Replace Johann-Jakob Schmid with:
Johann-Jakob Schmid United States
Seral Şahan Türkiye
Riad Taha Al-Kasasbeh Jordan
Karl Øyvind Mikalsen Norway
Rahime Ceylan Türkiye
Teresa Rocha Portugal
Saifur Rahman Australia
Héctor‐Gabriel Acosta‐Mesa Mexico
Vahid Ranaee Iran
Trang Pham Australia
German Prado-Arechiga relative to Johann-Jakob Schmid United States Johann-Jakob Schmid's profile →
Citations per field
00.5×2.5×
Johann-Jakob Schmid · 1×
Citations per year

Countries citing papers authored by German Prado-Arechiga

Since Specialization
Citations

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

Fields of papers citing papers by German Prado-Arechiga

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by German Prado-Arechiga. 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 German Prado-Arechiga. The network helps show where German Prado-Arechiga may publish in the future.

Co-authorship network of co-authors of German Prado-Arechiga

This figure shows the co-authorship network connecting the top 25 collaborators of German Prado-Arechiga. A scholar is included among the top collaborators of German Prado-Arechiga 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 German Prado-Arechiga. German Prado-Arechiga 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
#WorkIndexed citations
1 10
2 34
3 33
4 2
5 8
6 1
7 23
8 4
9 2
10 0
11 2
12 5
13 2
14 16
15 2
16 1
17 1
18 1
19 1
20 26

About German Prado-Arechiga

German Prado-Arechiga is a scholar working on Health Information Management, Statistics and Probability and Artificial Intelligence, having authored 24 papers that have together received 346 indexed citations. Recurring topics across this work include Fuzzy Logic and Control Systems (7 papers), Neural Networks and Applications (4 papers) and Artificial Intelligence in Healthcare (3 papers). The work is most often cited by research in Health Information Management (49 citations), Artificial Intelligence (199 citations) and Statistics and Probability (29 citations). German Prado-Arechiga has collaborated with scholars based in Mexico and China. Frequent co-authors include Patricia Melín, Juan Carlos Guzmán, Fevrier Valdez, Beatriz González and Martha Pulido. Their work appears in journals such as European Heart Journal, Expert Systems with Applications and Applied Soft Computing.

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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