Hugh Chen

9.5k citations
7 papers · 5.2k indexed · 2 hit papers · h-index 7

Hugh Chen

7 papers receiving 5.1k citations

Hit Papers

Algorithms to estimate Shapley value featur...199202020262022202410002.0k3.0k4.0k

Peers

Hugh Chen
Comparison fields: 5 of 214
  • Health Informatics 166
  • Artificial Intelligence 1.2k
  • Health Information Management 165
  • Environmental Engineering 445
  • Global and Planetary Change 422
Replace Gabriel Erion with:
Gabriel Erion United States
Nisha Bansal United States
Alex J. DeGrave United States
Jordan M. Prutkin United States
Ronit Katz United States
Jonathan Himmelfarb United States
Scott Lundberg United States
Bala G. Nair United States
Anne‐Laure Boulesteix Germany
Su‐In Lee United States
Hugh Chen relative to Gabriel Erion United States Gabriel Erion's profile →
Citations per field
00.5×1.5×
Gabriel Erion · 1×
Citations per year

Countries citing papers authored by Hugh Chen

Since Specialization
Citations

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

Fields of papers citing papers by Hugh Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 16 scholars most cited alongside Hugh Chen, 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 Hugh Chen Line = papers co-authored together Hugh Chen links everyone, so they are left out of the graph.

All Works

7 of 7 papers shown

About Hugh Chen

Hugh Chen is a scholar working on Health Information Management, Artificial Intelligence, Health, Toxicology and Mutagenesis, Computational Theory and Mathematics and Pediatrics, Perinatology and Child Health, having authored 7 papers that have together received 5.2k indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (4 papers), Machine Learning in Healthcare (3 papers), Bayesian Modeling and Causal Inference (2 papers), Machine Learning and Data Classification (2 papers), Computational Drug Discovery Methods (1 paper), Hemodynamic Monitoring and Therapy (1 paper), Gene expression and cancer classification (1 paper) and Artificial Intelligence in Healthcare (1 paper). The work is most often cited by research in Health Informatics (166 citations), Artificial Intelligence (1.2k citations), Health Information Management (165 citations), Environmental Engineering (445 citations) and Global and Planetary Change (422 citations). Hugh Chen has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Su‐In Lee, Scott Lundberg, Gabriel Erion, Jordan M. Prutkin, Ronit Katz, Nisha Bansal, Jonathan Himmelfarb, Bala G. Nair, Alex J. DeGrave and Ian Covert. Their work appears in journals such as Nature Machine Intelligence, The Lancet Healthy Longevity, Nature Biomedical Engineering, npj Digital Medicine and Nature Communications.

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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