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Machine-learning approach uncovers hemodynamic-driven phenotypes in cardiac surgery by clustering multimodal, high-dimensional perioperative data
0
Zitationen
13
Autoren
2025
Jahr
Abstract
Through a data-driven phenotypic analysis utilizing machine learning, various subgroups were identified among heterogeneous surgical patients, each displaying distinct characteristics linked to adverse outcomes. The integration of multi-dimensional intraoperative vital signs with perioperative data may support the development of more precise, individualized risk stratification and future perioperative management strategies.
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