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

Abstract

Sepsis remains one of the leading causes of morbidity and mortality worldwide, particularly among critically ill patients in intensive care units (ICUs). Traditional diagnostic approaches, such as the Sequential Organ Failure Assessment (SOFA) and systemic inflammatory response syndrome (SIRS) criteria, often detect sepsis after significant organ dysfunction has occurred, limiting the potential for early intervention. In this study, we reviewed how artificial intelligence (AI)-driven methodologies, including machine learning (ML), deep learning (DL), and natural language processing (NLP), can aid physicians. AI, in this case, particularly ML, processes massive amounts of real-time clinical data, vital signs, lab results, and patient history and can detect subtle patterns and predict sepsis earlier than traditional methods like SOFA or SIRS, which often lag behind after the presentation of the sequela. Models like random forest, XGBoost, and neural networks achieve high accuracy and area under the receiver operating characteristic curve (AUROC) scores (0.8-0.99) in ICU and emergency settings, enabling timely intervention by distinguishing sepsis from similar conditions despite the lack of perfect biomarkers. In practice, however, there are several potential pitfalls. Algorithmic bias due to nonrepresentative data, data fragmentation, lack of validation, and explainability issues are current barriers in developed models. Future research should address these limitations and develop more sophisticated models.

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