SEPSIS OF INFECTIOUS ORIGIN: EMERGING BIOMARKERS AND ARTIFICIAL INTELLIGENCE FOR EARLY DIAGNOSIS AND MORTALITY REDUCTION
DOI:
https://doi.org/10.63330/sasciencesv6n2-143Keywords:
Artificial intelligence, Biomarkers, Early diagnosis, Mortality, SepsisAbstract
Sepsis of infectious origin is one of the leading causes of morbidity and mortality in emergency departments and intensive care units, requiring early diagnosis and prompt therapeutic intervention to improve clinical outcomes. This study aims to analyze the scientific evidence regarding the use of emerging biomarkers and artificial intelligence-based tools as strategies for the early diagnosis of sepsis and mortality reduction. This narrative literature review was conducted through the analysis of scientific articles, international guidelines, and technical documents addressing sepsis, biomarkers, and artificial intelligence applications in clinical practice. The evidence indicates that biomarkers such as procalcitonin, presepsin, interleukin-6, C-reactive protein, and lactate have considerable potential to support the early identification of systemic inflammatory responses, particularly when combined with machine learning algorithms capable of integrating clinical, laboratory, and physiological data in real time. The reviewed studies also demonstrate that artificial intelligence models can improve diagnostic sensitivity, reduce the time required for sepsis recognition, and facilitate earlier therapeutic interventions, thereby contributing to lower hospital mortality rates. It is concluded that the integration of emerging biomarkers and artificial intelligence represents a promising strategy for improving early diagnosis, supporting clinical decision-making, and enhancing patient safety, although broader implementation still requires clinical validation, algorithm standardization, and adequate technological infrastructure.
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