Objective:

To develop a lightweight and flexible model for real-time sepsis prediction using vital signs in clinical settings.

Approach:

Key Findings:

• The MSTCL framework enables accurate online prediction of sepsis using only 6 vital signs.
• The model supports variable-length sequences, enhancing its applicability in real-time clinical settings.
• The approach demonstrates robust generalization and operational efficiency despite reduced input features.

Interpretation:

The proposed model shows that effective sepsis prediction is achievable with limited, easily obtainable physiological data.

Limitations:

• The reduction in input features may limit the model's ability to capture subtle physiological precursors of sepsis.
• The model's performance in diverse clinical environments needs further validation.

Conclusion:

The MSTCL framework represents a significant advancement in real-time sepsis prediction, leveraging minimal input data while maintaining predictive accuracy.

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