Temporality Detection

Temporality Detection is a computational task in natural language processing for phenotyping that identifies and classifies the temporal characteristics of clinical events within text, distinguishing it from other information extraction tasks by focusing specifically on the time-related status, such as whether an event is current, past, future, or hypothetical, in relation to the patient's timeline.

33

Publications

1.6K

Citations

112

Authors

58

Institutions

Publications per year

2017–2026

9

Authors

112

  1. XT
    Xavier Tannier

    Inserm, France

    4 publications · 172 citations

    4

    h-index

  2. SB
    Steven Bethard

    University of Arizona, United States

    3 publications · 235 citations

    3

    h-index

  3. SV
    Sumithra Velupillai

    King's College London, United Kingdom

    3 publications · 93 citations

    3

    h-index

  4. NV
    Natalia Viani

    King's College London, United Kingdom

    2 publications · 69 citations

    2

    h-index

  5. GS
    Guergana Savova

    Harvard University, United States

    2 publications · 111 citations

    2

    h-index

1–5 of 112

Venues

  1. Journal of Biomedical Informatics

    4 publications · 502 citations

    4

    h-index

  2. Journal of the American Medical Informatics Association

    JAMIA · 3 publications · 144 citations

    3

    h-index

  3. BMC Medical Informatics and Decision Making

    1 publications · 24 citations

    1

    h-index

  4. Expert Systems with Applications

    1 publications · 27 citations

    1

    h-index

  5. PubMed

    Repository · 1 publications · 10 citations

    1

    h-index

1–5