Data-Driven Modeling of Pregnancy-Related Complications
Camilo A. Espinosa, Martin G. Becker, Ivana Marić, Ronald J. Wong, Gary M. Shaw, Brice L Gaudilliere, Nima Aghaeepour, David K. Stevenson, Ina A. Stelzer, Laura S. Peterson, Alan Lee Chang, Maria Xenochristou, Thanaphong Phongpreecha, Davide De Francesco, Michael Katz, Yair J. Blumenfeld, Martin S. Angst · Trends in Molecular Medicine · 2021
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.