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Uncovering distinct clinical phenotypes in disseminated intravascular coagulation through machine learning-enabled cluster analysis
0
Zitationen
6
Autoren
2026
Jahr
Abstract
Background: Disseminated intravascular coagulation (DIC) is a critical condition encountered in the intensive care unit (ICU), characterized by multiple etiologies and variable outcomes. Distinguishing between DIC phenotypes poses a significant challenge. This study aims to apply unsupervised machine learning (ML) algorithms to stratify DIC patients, thereby enabling more personalized treatment approaches. Methods: -Plasmin Inhibitor Complex (PIC), tissue plasminogen activator-inhibitor complex (tPAIC), and thrombomodulin (TM). The elbow method, cumulative distribution function (CDF) plot, and consensus matrix were employed to ascertain the optimal number of clusters. Logistic regression (LR) analysis was used to investigate the association between the identified phenotypes and clinical endpoints. Results: ). Conclusion: The study identified two clusters with distinct laboratory profiles and mortality risk.
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