Outcome Phenotype Classification

Outcome Phenotype Classification is a supervised machine learning task that specifically involves the prediction of observable phenotypic outcomes from biological or clinical data, distinguished from other phenotype classification tasks by its focus on endpoints or resultant traits rather than intermediate or diagnostic phenotypes.

8

Publications

703

Citations

55

Authors

24

Institutions

Publications per year

2017–2026

2

Authors

55

  1. AR
    Aleksi Reito

    Coxa Hospital, Finland

    1 publications · 15 citations

    1

    h-index

  2. JS
    Jiao Sun

    Jilin University, China

    1 publications · 90 citations

    1

    h-index

  3. RA
    Ruth A. Carper

    San Diego State University, United States

    1 publications · 129 citations

    1

    h-index

  4. HD
    Harald De Cauwer

    University of Antwerp, Belgium

    1 publications · 23 citations

    1

    h-index

  5. EC
    Eric Courchesne

    University of California San Diego, United States

    1 publications · 129 citations

    1

    h-index

1–5 of 55

Venues

  1. 1

    h-index

  2. Annals of Neurology

    1 publications · 200 citations

    1

    h-index

  3. PubMed

    Repository · 1 publications · 74 citations

    1

    h-index

  4. Resuscitation

    1 publications · 23 citations

    1

    h-index

  5. The Journal Of Hand Surgery

    JHS · 1 publications · 15 citations

    1

    h-index

1–5