Machine Learning Technique Identifies and Classifies CKD Subtypes

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Even among patients with similar levels of kidney function, an algorithm that considers a host of characteristics—including demographics, biomarkers from blood and urine, health status and behaviors, and medication use—can categorize patients into three clinically distinguishable clusters associated with distinct outcomes, such as chronic kidney disease (CKD) progression, cardiovascular disease, and death, according to a new study in JASN.

This style of “subtyping” of CKD using “multi-dimensional patient data holds the key to precision medicine,” the authors write in “Subtyping CKD Patient by Consensus Clustering: The Chronic Renal Insufficiency Cohort (CRIC) Study.” The approach could provide a better

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