Machine learning models for abstract screening task - A systematic literature review application for health economics and outcome research.

Journal: BMC medical research methodology
PMID: 38724903

Abstract

OBJECTIVE: Systematic literature reviews (SLRs) are critical for life-science research. However, the manual selection and retrieval of relevant publications can be a time-consuming process. This study aims to (1) develop two disease-specific annotated corpora, one for human papillomavirus (HPV) associated diseases and the other for pneumococcal-associated pediatric diseases (PAPD), and (2) optimize machine- and deep-learning models to facilitate automation of the SLR abstract screening.

Authors

  • Jingcheng Du
    University of Texas Health Science Center at Houston, Houston, Texas, USA.
  • Ekin Soysal
    Intelligent Medical Objects, Houston, TX, USA.
  • Dong Wang
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Long He
    IMO Health, Inc., Rosemont, IL 60018, United States.
  • Bin Lin
    Department of Biostatistics, Hospital for Special Surgery, 535 E 70(th) Street, New York, NY 10021, United States of America.
  • Jingqi Wang
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Frank J Manion
    School of Biomedical Informatics, University of Texas Health Science Center, Houston, Texas, United States.
  • Yeran Li
    Harvard T.H. Chan School of Public Health, Cambridge, MA, USA.
  • Elise Wu
    Merck & Co., Inc, Rahway, NJ, USA.
  • Lixia Yao
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA.

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