Medicine and HealthJ. Clin. Med.
Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers
G. C, C. M, et al.
Discover how a groundbreaking machine-learning model developed by Garrido-Giménez et al. enhances the prediction of preterm preeclampsia using key biomarkers like sFlt-1/PlGF ratio, NT-proBNP, and uric acid, demonstrating improved predictive capabilities as delivery approaches.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Psychology
Urinary exosomal microRNAs as predictive biomarkers for persistent psychotic-like experiences
Y. Tomita, K. Suzuki, et al.
Interdisciplinary Studies
A deep-learning model for predictive archaeology and archaeological community detection
A. Resler, R. Yeshurun, et al.
Chemistry
Active coacervate droplets as a model for membraneless organelles and protocells
C. Donau, F. Späth, et al.
Biology
Discovery and remodeling of *Vibrio natriegens* as a microbial platform for efficient formic acid biorefinery
J. Tian, W. Deng, et al.
