Subphenotype Discovery

Subphenotype Discovery is a computational methodology within the domain of clinical phenotyping that identifies distinct, biologically meaningful subgroups of a disease characterized by heterogeneous manifestations through the systematic analysis of high-dimensional patient data, primarily leveraging natural language processing to extract and structure phenotypic information from unstructured electronic health records.

113

Publications

11.3K

Citations

1.2K

Authors

432

Institutions

Publications per year

2017–2026

63

Authors

1.2K

  1. CS
    Carolyn S. Calfee

    University of California, San Francisco, United States

    8 publications · 3.2K citations

    8

    h-index

  2. KD
    Kevin Delucchi

    University of California, San Francisco, United States

    7 publications · 2.9K citations

    7

    h-index

  3. YL
    Yuan Luo

    Northwestern University, United States

    7 publications · 304 citations

    6

    h-index

  4. MA
    Michael A. Matthay

    University of California, San Francisco, United States

    5 publications · 2K citations

    5

    h-index

  5. JA
    Jennifer A. Pacheco

    Northwestern University, United States

    5 publications · 1.3K citations

    5

    h-index

1–5 of 1.2K

Venues

  1. Bioinformatics

    4 publications · 96 citations

    4

    h-index

  2. Critical Care

    CC · 5 publications · 274 citations

    4

    h-index

  3. American Journal of Respiratory and Critical Care Medicine

    AJRCCM · 4 publications · 1.5K citations

    4

    h-index

  4. Inflammatory Bowel Diseases

    3 publications · 159 citations

    3

    h-index

  5. PLoS ONE

    3 publications · 721 citations

    3

    h-index

1–5