Development and validation of interpretable machine learning models to predict glomerular filtration rate in chronic kidney disease Colombian patients.
Journal:
Annals of clinical biochemistry
PMID:
39242084
Abstract
BACKGROUND: ML predictive models have shown their capability to improve risk prediction and assist medical decision-making, nevertheless, there is a lack of accuracy systems to early identify future rapid CKD progressors in Colombia and even in South America.