Arjun Magge

24 papers receiving 374 citations

Peers

Arjun Magge
Comparison fields: 5 of 71
  • Toxicology 42
  • Health Informatics 9
  • Artificial Intelligence 206
  • Geography, Planning and Development 20
  • Information Systems 35
Replace Davy Weissenbacher with:
Davy Weissenbacher United States
Ari Z Klein United States
Ai Kawazoe Japan
Serena Jeblee Canada
Ashlynn R. Daughton United States
Badisse Dahamna France
Todd Bodnar United States
Yu Lin United States
Herman Tolentino United States
Jeremy Ratcliff United Kingdom
Arjun Magge relative to Davy Weissenbacher United States Davy Weissenbacher's profile →
Citations per field
00.5×1.5×
Davy Weissenbacher · 1×
Citations per year

Countries citing papers authored by Arjun Magge

Since Specialization
Citations

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

Fields of papers citing papers by Arjun Magge

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201971
2 202146
3 202145
4 201733
5
Overview of the Fifth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at COLING 2020
202032
6 201928
7 202128
8 201823
9 201920
10 201811
11 201711
12 202311
13 202210
14
Clinical NER and Relation Extraction using Bi-Char-LSTMs and Random Forest Classifiers
201810
15 20229
16 20228
17 20188
18 20207
19 20186
20
CSaRUS-CNN at AMIA-2017 Tasks 1, 2: Under Sampled CNN for Text Classification.
20174

About Arjun Magge

Arjun Magge is a scholar working on Artificial Intelligence, Molecular Biology, Epidemiology, Information Systems and Infectious Diseases, having authored 24 papers that have together received 427 indexed citations. Recurring topics across this work include Topic Modeling (8 papers), Data-Driven Disease Surveillance (4 papers), Biomedical Text Mining and Ontologies (3 papers), Natural Language Processing Techniques (2 papers), Spam and Phishing Detection (2 papers), Misinformation and Its Impacts (2 papers), Genomics and Phylogenetic Studies (2 papers) and Zoonotic diseases and public health (1 paper). The work is most often cited by research in Toxicology (42 citations), Health Informatics (9 citations), Artificial Intelligence (206 citations), Geography, Planning and Development (20 citations) and Information Systems (35 citations). Arjun Magge has collaborated with scholars based in United States, Russia and United Kingdom. Frequent co-authors include Graciela Gonzalez‐Hernandez, Davy Weissenbacher, Karen O’Connor, Abeed Sarker, Ari Z Klein, Matthew Scotch, Ashlynn R. Daughton, Michael J. Paul, Elena Tutubalina and Zulfat Miftahutdinov. Their work appears in journals such as Journal of Medical Internet Research, Bioinformatics, Journal of the American Medical Informatics Association, Journal of Personalized Medicine and PLoS ONE.

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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