Background:
Sepsis is a life-threatening condition characterized by the body’s response to infection, leading to tissue damage, organ failure, and death. Early detection is essencial for improving patient results. Traditional diagnostic methods often not succesed in timely identification. Recent progress in AI offer promising opportunities for early sepsis prediction by analyzing hospital data.
Methodology:
This review synthesizes findings from five studies conducted in 2022-2024, focusing on AI applications in sepsis prediction. The methodologies include various machine learning models, including Random Forest, XGBoost, and Transformer-based architectures, applied to diverse datasets such as electronic health records, vital signs, laboratory results, and clinical notes. These studies used techniques like feature selection, data preprocessing, and model evaluation to enhance predictive accuracy.
Results:
• A meta-ensemble model combining Random Forest, XGBoost, and Decision Tree algorithms achieved an AUC-ROC of 0.96, outperforming individual models in early sepsis detection.
• The NAVOY® Sepsis algorithm demonstrated high predictive performance with an accuracy of 0.79, sensitivity of 0.80, and specificity of 0.78, predicting sepsis onset three hours in advance.
• A Transformer-based model combining physiological time series and clinical notes outperformed six baseline models, highlighting the effectiveness of multimodal data integration.
• An AI-driven tool developed by Johns Hopkins University reduced sepsis mortality by 20% by analyzing EHRs in real-time, despite an accuracy rate of 38%.
• A novel approach combining AI with a blood test identified molecular markers indicative of septic shock, offering a potential for fast and accurate diagnosis.
Conclusions:
The integration of AI in clinical settings shows great potential in developing predictors, thereby enabling early detection and improved treatment outcomes. Machine learning algorithms, particularly ensemble methods and deep learning techniques, have demonstrated high effectiveness in accurately predicting abnormalities. However, challenges remain in model interpretability, data quality, and integration with clinical workflows. Further research should focus on addressing these challenges to unlock AI’s full potential in sepsis management.
