Assertion Detection

Assertion Detection is a natural language processing task that identifies the evidential status or certainty of a clinical condition or phenotype in text by classifying assertions into categories such as present, absent, possible, or hypothetical.

26

Publications

2.3K

Citations

86

Authors

31

Institutions

Publications per year

2017–2026

8

Variants

Assertion Status Detection

Authors

86

  1. SV
    Shobha Vasudevan

    University of Illinois Urbana-Champaign, United States

    4 publications · 335 citations

    4

    h-index

  2. GP
    Graziano Pravadelli

    University of Verona, Italy

    2 publications · 75 citations

    2

    h-index

  3. SJ
    Sanjay J. Patel

    University of Illinois Urbana-Champaign, United States

    2 publications · 213 citations

    2

    h-index

  4. DJ
    Daniel Johnson

    University of Illinois Urbana-Champaign, United States

    2 publications · 213 citations

    2

    h-index

  5. DS
    David Sheridan

    University of Illinois Urbana-Champaign, United States

    2 publications · 185 citations

    2

    h-index

1–5 of 86

Venues

  1. Journal of the American Medical Informatics Association

    JAMIA · 3 publications · 1.3K citations

    3

    h-index

  2. 1

    h-index

  3. Asia and South Pacific Design Automation Conference

    Conference · 1 publications · 42 citations

    1

    h-index

  4. 1

    h-index

  5. Journal of Biomedical Informatics

    1 publications · 62 citations

    1

    h-index

1–5