Bloodstream infections caused by spp. are among the leading hospital-acquired infections, but their diagnosis remains slow and challenging with conventional culture-based methods, which often require days to deliver results. This study aimed to deve...
Candidemia is a life-threatening bloodstream infection associated with high mortality rates, particularly in critically ill patients. Accurate risk stratification is crucial for timely intervention and could improve patient outcomes. This study aimed...
Candidemia often poses a diagnostic challenge due to the lack of specific clinical features, and delayed antifungal therapy can significantly increase mortality rates, particularly in the intensive care unit (ICU). This study aims to develop a machin...
Federated learning (FL) has emerged as a significant method for developing machine learning models across multiple devices without centralized data collection. Candidemia, a critical but rare disease in ICUs, poses challenges in early detection and t...
OBJECTIVES: Candidemia is the most frequent invasive fungal disease and the fourth most frequent bloodstream infection in hospitalized patients. Its optimal management is crucial for improving patients' survival. The quality of candidemia management ...
In this narrative review, we discuss studies assessing the use of machine learning (ML) models for the early diagnosis of candidemia, focusing on employed models and the related implications. There are currently few studies evaluating ML techniques f...
PURPOSE: Candidemia is a highly lethal infection; several scores have been developed to assist the diagnosis process and recently different models have been proposed. Aim of this work was to assess predictive performance of a Random Forest (RF) algor...
It is evident that a robust immune system keeps Candida albicans infection in check, but weakened immunity opens the door for shifting from a benign yeast form to an invasive hyphal form which leads to systemic candidiasis with high mortality rate. H...
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