Widely accessible prognostication using medical history for fetal growth restriction and small for gestational age in nationwide insured women.

ABSTRACT: Prevention of fetal growth restriction/small for gestational age (FGR/SGA) is adequate if screening is accurate. Ultrasound and biomarkers can achieve this goal; however, both are often inaccessible. This study aimed to develop, validate, and deploy a prognostic prediction model for screening FGR/SGA using only medical history. From a nationwide health insurance database (n = 1,697,452), we retrospectively selected visits to 22,024 healthcare providers of primary, secondary, and tertiary care. This study used machine learning (including deep learning) to develop prediction models using 54 medical-history predictors. After evaluating model calibration, clinical utility, and explainability, we selected the best by discrimination ability. We also externally validated the models using geograph

SUBMITTER: Sufriyana H 

PROVIDER: S-EPMC11894118 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature