Unsupervised Phenotype Discovery

Unsupervised Phenotype Discovery is a computational method within the domain of computational phenotyping that identifies novel, data-driven patient subgroups from unlabeled electronic health records through pattern recognition without prior clinical labels or supervision.

138

Publications

11.1K

Citations

1.7K

Authors

540

Institutions

Publications per year

2017–2026

118

Authors

1.7K

  1. YL
    Yuan Luo

    Northwestern University, United States

    5 publications · 292 citations

    4

    h-index

  2. WC
    Wendy C. Moore

    Wake Forest University, United States

    3 publications · 2.6K citations

    3

    h-index

  3. HE
    Hossein Estiri

    Massachusetts General Hospital, United States

    5 publications · 393 citations

    3

    h-index

  4. NJ
    Neil J. Sebire

    University College London, United Kingdom

    3 publications · 188 citations

    3

    h-index

  5. CT
    Charat Thongprayoon

    Mayo Clinic, United States

    2 publications · 71 citations

    3

    h-index

1–5 of 1.7K

Venues

  1. PLoS ONE

    6 publications · 141 citations

    6

    h-index

  2. Journal of Biomedical Informatics

    4 publications · 289 citations

    4

    h-index

  3. Scientific Reports

    4 publications · 168 citations

    4

    h-index

  4. medRxiv

    Repository · 2 publications · 59 citations

    3

    h-index

  5. American Journal of Respiratory and Critical Care Medicine

    AJRCCM · 3 publications · 2.3K citations

    3

    h-index

1–5