Machine Learning Screening

Machine Learning Screening is a computational methodology within the domain of systematic study selection that employs machine learning algorithms to automatically identify, classify, or prioritize scholarly records for inclusion in academic research, distinguished by its reliance on data-driven models to enhance the efficiency and accuracy of screening processes.

133

Publications

8.1K

Citations

710

Authors

325

Institutions

Publications per year

2017–2026

121

Authors

710

  1. AW
    Abdul Wadood

    Abdul Wali Khan University Mardan, Pakistan

    4 publications · 160 citations

    4

    h-index

  2. LH
    Lisa Hartling

    University of Alberta, Canada

    4 publications · 398 citations

    4

    h-index

  3. AG
    Allison Gates

    University of Alberta, Canada

    4 publications · 398 citations

    4

    h-index

  4. RV
    Rens van de Schoot

    Utrecht University, Netherlands

    4 publications · 301 citations

    4

    h-index

  5. HL
    Hong Liang

    Guangzhou University, China

    3 publications · 269 citations

    3

    h-index

1–5 of 710

Venues

  1. Systematic Reviews

    12 publications · 1.2K citations

    12

    h-index

  2. ACS Applied Materials & Interfaces

    4 publications · 213 citations

    4

    h-index

  3. BMC Medical Research Methodology

    4 publications · 391 citations

    4

    h-index

  4. The Journal of Physical Chemistry C

    3 publications · 72 citations

    3

    h-index

  5. PLoS ONE

    3 publications · 71 citations

    3

    h-index

1–5