Data-driven spatial-temporal framework for exploring the heterogeneity and temporality of sepsis.

Sepsis remains a leading cause morbidity and mortality worldwide; effective targeted therapies remain elusive due to its inherent heterogeneity and dynamic temporal evolution. Existing frameworks often focus on either the diverse manifestations of sepsis or its progression over time, but fail to integrate these critical aspects. In this review, we propose a novel spatial-temporal framework that integrates both the heterogeneity and temporality of sepsis. The framework consists of two key dimensions: The cross-sectional (heterogeneity) dimension, which addresses pathogen variability, host factors, and pathogen-host interactions; The longitudinal (temporality) dimension, which explores the dynamic evolution of sepsis and the need for adaptive, real-time interventions. Given the complexity of multidimensional temporal data, big data techniques have the potential to integrate these data and decompose sepsis into distinct disease subtypes. Stratification facilitates the development of personalized therapeutic approaches tailored to specific subtypes. Moreover, methods, such as reinforcement learning, can track the dynamic transitions between these subtypes, enabling real-time adaptation of treatment strategies.