Explainable time-series machine learning for early sepsis prediction and risk stratification: a multi-dataset computational study

This study presents an explainable, multi-dataset computational framework that integrates phenotypic clustering with Long Short-Term Memory (LSTM) models and dynamic feature importance analysis to achieve robust early sepsis prediction and risk stratification while providing clinically interpretable insights into disease progression.