Gabriel Erion

10.0k citations
5 papers · 5.5k · 1 hit paper · h-index 5

Impact in

    • Artificial Intelligence in Healthcare and Education
    • Explainable Artificial Intelligence (XAI)
    • Machine Learning in Healthcare
    • Machine Learning and Data Classification
    • Anomaly Detection Techniques and Applications

Papers in

    • Machine Learning in Healthcare 3
    • Explainable Artificial Intelligence (XAI) 2
    • Bayesian Modeling and Causal Inference 1
    • Healthcare Technology and Patient Monitoring 2
    • Hemodynamic Monitoring and Therapy 1

Gabriel Erion

5 papers receiving 5.4k citations

Gabriel Erion's Hit Papers

From local explanations to global understanding with explainable AI for trees 2020 · 5.4k citations
5.4k0+2+4Years since publication10002.0k3.0k4.0k5.0k

Peers

Gabriel Erion
Comparison fields: 5 of 210
  • Health Informatics 132
  • Artificial Intelligence 1.2k
  • Environmental Engineering 444
  • Health Information Management 107
  • Global and Planetary Change 442
Replace Nisha Bansal with:
Nisha Bansal United States
Jordan M. Prutkin United States
Hugh Chen United States
Ronit Katz United States
Alex J. DeGrave United States
Bala G. Nair United States
Scott Lundberg United States
Anne‐Laure Boulesteix Germany
Su‐In Lee United States
Yuantong Gu Australia
Gabriel Erion relative to Nisha Bansal United States Nisha Bansal's profile →
Citations per field
00.5×1.5×
Nisha Bansal · 1×
Citations per year

Countries citing papers authored by Gabriel Erion

Since Specialization
Citations

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

Fields of papers citing papers by Gabriel Erion

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 17 scholars most cited alongside Gabriel Erion, 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 Gabriel Erion Line = papers co-authored together Gabriel Erion links everyone, so they are left out of the graph.

All Works

5 of 5 papers shown

About Gabriel Erion

Gabriel Erion is a scholar working on Artificial Intelligence, Surgery, Infectious Diseases, Virology and Agronomy and Crop Science, having authored 5 papers that have together received 5.5k indexed citations. Recurring topics across this work include Machine Learning in Healthcare (3 papers), Healthcare Technology and Patient Monitoring (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), HIV/AIDS Research and Interventions (1 paper), HIV Research and Treatment (1 paper), Bayesian Modeling and Causal Inference (1 paper), Quality and Safety in Healthcare (1 paper) and Hemodynamic Monitoring and Therapy (1 paper). The work is most often cited by research in Health Informatics (132 citations), Artificial Intelligence (1.2k citations), Environmental Engineering (444 citations), Health Information Management (107 citations) and Global and Planetary Change (442 citations). Gabriel Erion has collaborated with scholars based in United States, France and United Kingdom. Frequent co-authors include Su‐In Lee, Scott Lundberg, Hugh Chen, Jonathan Himmelfarb, Nisha Bansal, Ronit Katz, Alex J. DeGrave, Bala G. Nair, Jordan M. Prutkin and Joseph D. Janizek. Their work appears in journals such as PLoS ONE, Nature Biomedical Engineering, npj Digital Medicine and Nature Machine Intelligence.

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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