Zhang Yingli

Machine learning clustering of multidimensional clinical features identifies four prognostically distinct sepsis subtypes

Sepsis is highly heterogeneous, and traditional “one-size-fits-all” treatment approaches are insufficient for optimal risk stratification. Using the MIMIC-III database, we employed unsupervised machine learning (k-means clustering) based on eight key clinical features (age, SOFA...