Optimization-enhanced Gaussian mixture modeling for data-driven subphenotyping of septic shock

= 237) presented relatively milder clinical profiles. Compared with the conventional clustering methods, the proposed framework demonstrated improved clustering performance, achieving a silhouette coefficient of 0.8528 ± 0.0112 across repeated runs, indicating stable subtype separation. Overall, this study provides a data-driven methodological framework for septic shock subphenotyping, which may contribute to improved understanding of patient heterogeneity and support future research in clinical stratification.