Temporal validation of machine learning models for pre-eclampsia prediction using routinely collected maternal characteristics: A validation study.

Journal: Computers in biology and medicine
PMID: 40228443

Abstract

BACKGROUND: Pre-eclampsia (PE) contributes to more than one-fourth of all maternal deaths and half a million newborn deaths worldwide every year. Early screening and interventions can reduce PE incidence and related complications. We aim to 1) temporally validate three existing models (two machine learning (ML) and one logistic regression) developed in the same region and 2) compare the performances of the validated ML models with the logistic regression model in PE prediction. This work addresses a gap in the literature by undertaking a comprehensive evaluation of existing risk prediction models, which is an important step to advancing this field.

Authors

  • Sofonyas Abebaw Tiruneh
    Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
  • Daniel Lorber Rolnik
    Department of Obstetrics and Gynecology, Monash University, Melbourne, VIC, Australia.
  • Helena Teede
    Monash Partners Academic Health Science Centre, Melbourne, VIC, Australia.
  • Joanne Enticott
    Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia. joanne.enticott@monash.edu.

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