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Sepsis is a leading cause of ICU mortality, and early detection is critical for improving patient outcomes. However, existing machine learning models often rely on hourly aggregated data, limiting their ability to capture rapid physiological changes, and frequently lack interpretability, reducing clinical trust and usability. This paper proposes a conceptual framework that integrates Temporal Convolutional Networks (TCNs) with an attention mechanism to analyze high-frequency, minute-level vital sign data for early sepsis prediction. The architecture includes a data input layer, a TCN-based feature extractor with causal dilated convolutions and residual connections, an attention module for identifying clinically relevant time points and variables, and a prediction head that estimates the risk of sepsis within a 6-hour horizon. The proposed approach enables efficient parallel processing, improved temporal sensitivity, and enhanced interpretability compared to recurrent models. While offering advantages in real-time prediction and explainability, challenges remain in handling missing data, ensuring generalizability across ICUs, and minimizing false alarms for clinical deployment.

Sepsis remains a major cause of mortality in intensive care units, largely due to delayed recognition and the limitations of current machine learning models that rely on retrospective, static electronic health record data. Although these models often show strong offline performance, their clinical translation is constrained by mismatches between training conditions and real-time bedside environments. Most existing systems depend on hourly aggregates or batch processing, introducing delays that reduce their usefulness within the narrow therapeutic window for intervention. In contrast, continuous vital sign streams generated by modern bedside monitors represent an underused source of real-time physiological information. This perspective argues that effective sepsis prediction requires a shift toward edge AI architectures that enable low-latency, privacy-preserving inference directly at the point of care. By treating physiological signals as continuous data streams rather than static records, and by deploying computation at the bedside instead of centralized cloud systems, models can better align with clinical realities. Such an approach could improve early detection, reduce alert fatigue through more context-aware predictions, and mitigate privacy, latency, and bandwidth challenges associated with cloud-based solutions. Ultimately, transitioning from retrospective modeling to real-time, edge-enabled decision support represents a necessary evolution in clinical AI, requiring close collaboration between clinicians, engineers, and data scientists to enable deployable, trustworthy, and timely sepsis prediction systems.