Knowledge-guided Transfer Prediction In Underrepresented Populations: A GRU-D-Static Framework For Maternal And Neonatal Outcomes

Integrating summary-level scientific knowledge into neural network models provides a practical strategy for transferring prediction models trained on adequately sampled source cohorts to underrepresented target populations, where individual-level data in the target domain are often limited or unavailable. In this study, we propose transfer prediction strategies incorporating external summary-level scientific knowledge and illustrate its application on the PRISMA Maternal and Neonatal Health Study, training a neural network model on the source data to predict adverse outcomes in the target cohorts. Besides, we also extend the existing GRU-D framework by incorporating static feature embeddings and attention weights to jointly leverage temporal and static information for improved prediction. Our approach employs soft labels derived from summary-level statistics describing the target population to fine-tune GRU-D-Static models that are initially trained on the source populations. We evaluate six maternal and neonatal outcomes, including stillbirth, preterm birth, low birth weight, small vulnerable newborn, neonatal death, and maternal near miss. Across all tested scenarios, fine-tuning using soft labels from just basic covariates substantially improved predictive performance compared with deep learning models trained on the source sample. Furthermore, the performance slightly improves more when additional covariates were incorporated into the logistic regression model or when partial input features from the target population were available for fine-tuning. These findings demonstrate that integrating existing scientific knowledge in the literature through transfer prediction of source neural network models can enhance prediction performance in underrepresented target populations, reducing reliance on large-scale data collection and supporting risk prediction in global health.