ArticleClinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
Development and Validation of an Early Risk Prediction Model for Sepsis-Induced Coagulopathy Based on Machine Learning.
Article in Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
ObjectiveTo develop and validate machine learning models for early prediction of sepsis-induced coagulation dysfunction (SIC) risk at intensive care unit (ICU) admission, before diagnostic criteria are fully met.MethodsThis retrospective cohort study enrolled 197 septic ICU patients. The cohort was randomly split into training (n=159, 80.7%) and internal validation sets (n=38, 19.3%). An independent external cohort of 91patients served for validation. Feature screening used least absolute shrinkage and selection operator (LASSO) followed by multivariate logistic regression (
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