UNMASKING THE HIDDEN PATTERNS: MACHINE LEARNING IDENTIFIES AND PREDICTS CLUSTERS WITH DISTINCT PROFILES AND OUTCOMES IN ACUTE-ON-CHRONIC LIVER FAILURE (CLUSTER-ACLF)
<div><p><strong><b>Background:</strong> </b>Heterogeneity among patients with acute-on-chronic liver failure (ACLF) confer variable outcomes (mortality-range: 0-100%). While prognostic scores capture the known associations, machine learning (ML) can identify the intricate hidden patterns between patient characteristics without any explicit hypothesis or labelling that remain unexplored in ACLF. We employed ML to explore, describe, and predict unknown clusters in ACLF patients.</p>
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