Clinical Embedding Learning

Clinical Embedding Learning is a computational methodology in unsupervised phenotype discovery that maps clinical data into low-dimensional vector representations to preserve semantic relationships and facilitate the identification of latent patient subgroups.

42

Publications

3.3K

Citations

189

Authors

90

Institutions

Publications per year

2017–2026

39

Authors

189

  1. XJ
    Xiaoqian Jiang

    University of California San Diego, United States

    3 publications · 112 citations

    3

    h-index

  2. MG
    Marzyeh Ghassemi

    Massachusetts Institute of Technology, United States

    2 publications · 143 citations

    2

    h-index

  3. IS
    Isaac S. Kohane

    Harvard University, United States

    2 publications · 81 citations

    2

    h-index

  4. CM
    Chengsheng Mao

    Northwestern University, United States

    2 publications · 78 citations

    2

    h-index

  5. MB
    Matthew B. A. McDermott

    University of Toronto, Canada

    2 publications · 848 citations

    2

    h-index

1–5 of 189

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 788 citations

    4

    h-index

  2. Computers in Biology and Medicine

    3 publications · 65 citations

    3

    h-index

  3. Journal of Biomedical Informatics

    3 publications · 198 citations

    3

    h-index

  4. Neurocomputing

    2 publications · 91 citations

    2

    h-index

  5. Expert Systems with Applications

    1 publications · 16 citations

    1

    h-index

1–5