Temporal validation of machine learning models for pre-eclampsia prediction using routinely collected maternal characteristics: A validation study
Sofonyas Abebaw Tiruneh; Daniel L. Rolnik; Helena Teede; Joanne Enticott · 2025 · Computers in Biology and Medicine
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
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 mod
Abstract by Sofonyas Abebaw Tiruneh; Daniel L. Rolnik; Helena Teede; Joanne Enticott, Computers in Biology and Medicine (2025) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1016/j.compbiomed.2025.110183