Machine Learning Surveillance

Machine Learning Surveillance is a scholarly contribution in the domain of electronic health record research that employs algorithmic models to systematically monitor, analyze, and interpret health data for patterns, anomalies, or trends, distinguishing itself through the application of adaptive learning techniques to automate and enhance surveillance processes.

108

Publications

5.7K

Citations

502

Authors

291

Institutions

Publications per year

2017–2026

98

Authors

502

  1. TT
    Thanh Thi Nguyen

    Deakin University, Australia

    3 publications · 307 citations

    3

    h-index

  2. KS
    KC Santosh

    University of South Dakota, United States

    2 publications · 340 citations

    2

    h-index

  3. JT
    Jack Teitel

    University of Rochester, United States

    2 publications · 94 citations

    2

    h-index

  4. AK
    Atif Khan

    Islamia College University, Pakistan

    2 publications · 79 citations

    2

    h-index

  5. UP
    Umapada Pal

    Indian Statistical Institute, India

    2 publications · 340 citations

    2

    h-index

1–5 of 502

Venues

  1. IEEE Access

    5 publications · 223 citations

    5

    h-index

  2. PLoS ONE

    4 publications · 162 citations

    4

    h-index

  3. arXiv (Cornell University)

    Repository · 2 publications · 260 citations

    2

    h-index

  4. Applied Sciences

    2 publications · 133 citations

    2

    h-index

  5. Healthcare

    2 publications · 92 citations

    2

    h-index

1–5