Computational Phenotyping Algorithms

Computational Phenotyping Algorithms is a systematic method that leverages computational techniques to derive and define disease phenotypes from complex biomedical data by identifying meaningful patterns indicative of clinical traits, thereby distinguishing it from general data analysis approaches through its specific focus on phenotype inference in healthcare and life sciences research.

109

Publications

8.4K

Citations

838

Authors

401

Institutions

Publications per year

2017–2026

77

Authors

838

  1. JC
    Joshua C. Denny

    Vanderbilt University, United States

    11 publications · 2.8K citations

    11

    h-index

  2. JA
    Jennifer A. Pacheco

    Northwestern University, United States

    9 publications · 1.3K citations

    9

    h-index

  3. JP
    Jyotishman Pathak

    Cornell University, United States

    9 publications · 714 citations

    9

    h-index

  4. LV
    Luke V. Rasmussen

    Northwestern University, United States

    8 publications · 1K citations

    8

    h-index

  5. MH
    Melissa Haendel

    Oregon Health & Science University, United States

    6 publications · 405 citations

    6

    h-index

1–5 of 838

Venues

  1. Journal of the American Medical Informatics Association

    JAMIA · 12 publications · 1.6K citations

    12

    h-index

  2. PubMed

    Repository · 6 publications · 368 citations

    6

    h-index

  3. Journal of Biomedical Informatics

    5 publications · 281 citations

    5

    h-index

  4. Scientific Reports

    5 publications · 116 citations

    5

    h-index

  5. Bioinformatics

    3 publications · 1.4K citations

    3

    h-index

1–5