Benjamin Kompa

802 citations
8 papers · 338 · 1 hit paper · h-index 6

Impact in

    • Artificial Intelligence in Healthcare and Education
  • Toxicology top 10%
    • Pharmacovigilance and Adverse Drug Reactions

Papers in

Benjamin Kompa

7 papers receiving 334 citations

Benjamin Kompa's Hit Papers

Second opinion needed: communicating uncertainty in medical machine learning 2021 · 212 citations
2120+1+3Years since publication50100150200

Peers

Benjamin Kompa
Comparison fields: 5 of 113
  • Health Informatics 76
  • Toxicology 26
  • Artificial Intelligence 128
  • Health Information Management 17
  • Family Practice 7
Replace Hanyin Wang with:
Hanyin Wang United States
Lin Guo China
Yikuan Li United States
André Carrington Canada
Maryam Zolnoori United States
Yaara Goldschmidt Israel
Pantelis Natsiavas Greece
Louise Deléger France
Surabhi Datta United States
Andrew A. S. Soltan United Kingdom
Benjamin Kompa relative to Hanyin Wang United States Hanyin Wang's profile →
Citations per field
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Citations per year

Countries citing papers authored by Benjamin Kompa

Since Specialization
Citations

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

Fields of papers citing papers by Benjamin Kompa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

8 of 8 papers shown
#Work
1
Second opinion needed: communicating uncertainty in medical machine learning
Hit paper breakdown →
2021212
2 202241
3 202439
4
Clinical Concept Embeddings Learned from Massive Sources of Medical Data.
201817
5 202115
6 201713
7 20191
8 20250

About Benjamin Kompa

Benjamin Kompa is a scholar working on Artificial Intelligence, Molecular Biology, Health Informatics, General Health Professions and Family Practice, having authored 8 papers that have together received 338 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (2 papers), Pharmacovigilance and Adverse Drug Reactions (1 paper), Ocular Surface and Contact Lens (1 paper), Natural Language Processing Techniques (1 paper), COVID-19 diagnosis using AI (1 paper), Computational Drug Discovery Methods (1 paper), Glaucoma and retinal disorders (1 paper) and Cell Image Analysis Techniques (1 paper). The work is most often cited by research in Health Informatics (76 citations), Toxicology (26 citations), Artificial Intelligence (128 citations), Health Information Management (17 citations) and Family Practice (7 citations). Benjamin Kompa has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Andrew L. Beam, Jasper Snoek, Rudraksh Tuwani, David M. Levine, Samuel G. Finlayson, Ateev Mehrotra, Michael R. Smith, Paul Bain, Stephen Woloszynek and Jeffery L. Painter. Their work appears in journals such as npj Digital Medicine, Drug Safety, The Lancet Digital Health, PLoS ONE and PubMed.

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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