Identifying Cardiogenic Shock Sub-Phenotypes with Machine Learning: A Multicenter Study Combining Clinical and Echocardiographic Data
Anno:
2025
Background: sub-phenotyping patients with cardiogenic shock (CS) through a non-traditional clustering method may represent a significant step forward in precision medicine, enhancing clinical outcomes in this heterogeneous and high-mortality condition. We aimed to apply an unsupervised machine learning approach integrating clinical and imaging data (including advanced echocardiography) to identify…