1887
Volume 2023, Issue 2
  • EISSN: 2220-2749

Abstract

Sepsis, a life-threatening condition triggered by an altered immune response to infection, poses significant challenges in clinical management.

This review discusses the role of Artificial Intelligence (AI) in predicting Sepsis-Associated Encephalopathy (SAE) and Sepsis-Associated Delirium (SAD).

A thorough search encompassing PubMed, CINAHL, Medline, and Google Scholar yielded studies published from 2010 to 2023.

The narrative review emphasizes AI's potential in the early identification and prognosis of SAE and SAD, specifically through machine learning and deep learning methods, such as XGBoost.

This review underscores the importance of early detection in sepsis and emphasizes how AI can improve prediction accuracy, offering promise in transforming the management of these complex neurological complications within the intensive care unit (ICU).

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2024-01-10
2024-06-15
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