Data-Driven Modeling of Pregnancy-Related Complications

Abstract

A healthy pregnancy depends on complex interrelated biological adaptations involving placentation, maternal immune responses, and hormonal homeostasis. Recent advances in high-throughput technologies have provided access to multiomics biological data that, combined with clinical and social data, can provide a deeper understanding of normal and abnormal pregnancies. Integration of these heterogeneous datasets using state-of-the-art machine-learning methods can enable the prediction of short- and long-term health trajectories for a mother and offspring and the development of treatments to prevent or minimize complications. We review advanced machine-learning methods that could: provide deeper biological insights into a pregnancy not yet unveiled by current methodologies; clarify the etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest the best approaches to address disparities in outcomes affecting vulnerable populations.

Keywords: machine learning; maternal health; multimodal learning; multiomics; multitask learning; pregnancy; systems biology.

Figure 1.. Incorporating Diverse Data Modalities to Build Holistic Models of Pregnancy Biology.

The various factors which influence maternal and fetal health during gestation are measured to generate diverse intercorrelated types of data. Machine-learning methods can be used to develop holistic models of maternal and fetal biology that capture the complex interactions between these modalities, reveal mechanistic insight into various adverse outcomes, and assist in diagnostics, therapeutics, and generation of predictive analytics.