Free-text Mining

Free-Text Mining is a computational methodology within the domain of public health surveillance that involves the systematic extraction and analysis of unstructured textual data from clinical or epidemiological records to identify and characterize Influenza-Like Illness indicators, distinguishing it from structured data analysis by its focus on natural language patterns and context-dependent symptom representation.

328

Publications

17.8K

Citations

1.4K

Authors

483

Institutions

Publications per year

2017–2026

130

Authors

1.4K

  1. GN
    Goran Nenadić

    University of Manchester, United Kingdom

    5 publications · 180 citations

    5

    h-index

  2. LZ
    Li Zhou

    Brigham and Women's Hospital, United States

    4 publications · 155 citations

    4

    h-index

  3. RD
    Richard Dobson

    King's College London, United Kingdom

    4 publications · 139 citations

    4

    h-index

  4. BR
    Brett R. South

    University of Utah, United States

    4 publications · 134 citations

    4

    h-index

  5. MH
    Matthew H. Samore

    University of Utah, United States

    4 publications · 130 citations

    4

    h-index

1–5 of 1.4K

Venues

  1. PubMed

    Repository · 26 publications · 817 citations

    18

    h-index

  2. PLoS ONE

    11 publications · 315 citations

    11

    h-index

  3. Journal of Medical Internet Research

    6 publications · 594 citations

    6

    h-index

  4. Journal of the American Medical Informatics Association

    JAMIA · 6 publications · 398 citations

    6

    h-index

  5. BMC Bioinformatics

    5 publications · 311 citations

    5

    h-index

1–5