Supervised Phenotype Classification

Supervised Phenotype Classification is a supervised machine learning approach within computational phenotyping that learns predictive models from labeled clinical data to assign predefined phenotype labels (such as disease status or clinical traits) to individual patients or patient records.

27

Publications

1K

Citations

219

Authors

126

Institutions

Publications per year

2017–2026

20

Authors

219

  1. DW
    Daniel W. Gerlich

    Austrian Academy of Sciences, Austria

    2 publications · 107 citations

    2

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  2. RH
    Rudolf Hoefler

    Austrian Academy of Sciences, Austria

    2 publications · 107 citations

    2

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  3. CS
    Christoph Sommer

    Kantonsspital Graubünden, Switzerland

    2 publications · 107 citations

    2

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  4. MS
    Matthias Samwer

    Austrian Academy of Sciences, Austria

    2 publications · 107 citations

    2

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  5. SS
    Sheng‐Feng Sung

    Chia-Yi Christian Hospital, Taiwan

    1 publications · 63 citations

    1

    h-index

1–5 of 219

Venues

  1. PLoS ONE

    3 publications · 168 citations

    3

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  2. Scientific Reports

    2 publications · 80 citations

    2

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  3. Journal of the American Medical Informatics Association

    JAMIA · 2 publications · 52 citations

    2

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  4. Addictive Behaviors

    1 publications · 51 citations

    1

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  5. 1

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1–5