Avicenna - الأعداد السابقة

This editorial addresses the tragic collapse of Gaza’s health system resulting from the recent conflict between Israel and Palestine. The deliberate targeting of healthcare facilities, atrocities committed by the Israeli army, and the dire humanitarian crisis have led to catastrophic conditions. The toll includes over 20,000 dead, 1.9 million displaced, and countless injuries, with healthcare facilities operating beyond capacity. The health system’s collapse necessitates patient transfer to other countries. International law violations are noted, especially the deliberate targeting of health facilities. The editorial emphasizes the urgent need for the international community’s intervention, calling for an immediate ceasefire, protection of health workers, and accountability for potential war crimes. The ultimate goal is to uphold human rights, protect civilians, and rebuild Gaza’s health infrastructure for lasting stability.

The use of data for healthcare decision-making has numerous benefits, including increasing knowledge of user demographics and needs, enabling adequate planning of healthcare resources and services, and providing a roadmap of decisions made to ensure stakeholder accountability. Despite these clear benefits, frameworks and theories guiding decision making in healthcare remain under-utilised. This paper presents three decision-making theories that focus on data. Classical Decision Theory and its modern iterations emphasize the decision-making process and the use of data in this process. The Ottawa Decision Support Framework is employed when the decision relates to new diagnoses or treatments or when extensive deliberation is needed in uncertain circumstances. Lastly, Bayesian Decision Theory considers existing knowledge and cost functions in decision-making. The context in which these theories were developed and applied is discussed, and their future applications in healthcare decision-making are explored.

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

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

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

Results: 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.

Conclusion: 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).