Exploring the Potentials of Artificial Intelligence in Sepsis Management in the Intensive Care Unit

Area of Science:

• Critical Care Medicine
• Medical Informatics
• Biomedical Data Science

Background:

• Sepsis is a major global cause of death, especially in intensive care units (ICUs).
• Current diagnostic tools like SOFA and SIRS criteria often identify sepsis late, hindering early treatment.
• There is a need for advanced methods to detect sepsis sooner.

Purpose of the Study:

• To review how artificial intelligence (AI) methodologies can assist in early sepsis detection.
• To explore the potential of machine learning (ML), deep learning (DL), and natural language processing (NLP) in sepsis diagnosis.

Main Methods:

• Review of AI-driven methodologies, focusing on ML algorithms.
• Analysis of AI's ability to process real-time clinical data, vital signs, lab results, and patient history.
• Evaluation of model performance metrics like accuracy and AUROC scores.

Main Results:

• AI, particularly ML, can detect subtle patterns to predict sepsis earlier than SOFA or SIRS criteria.
• Models such as random forest, XGBoost, and neural networks show high accuracy (0.8-0.99 AUROC) in predicting sepsis.
• AI aids in distinguishing sepsis from other conditions, enabling timely interventions.

Conclusions:

• AI offers a promising approach for earlier and more accurate sepsis detection in critical care settings.
• Challenges such as algorithmic bias, data fragmentation, and lack of explainability need to be addressed.
• Future research should focus on refining AI models to overcome current limitations for clinical implementation.