首页|University of Chinese Academy of Sciences Reports Findings in Machine Learning ( Identifying the risk factors of ICU-acquired fungal infections: clinical evidenc e from using machine learning)
University of Chinese Academy of Sciences Reports Findings in Machine Learning ( Identifying the risk factors of ICU-acquired fungal infections: clinical evidenc e from using machine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating in Shenzhen, Peop le’s Republic of China, by NewsRx journalists, research stated, “Fungal infectio ns are associated with high morbidity and mortality in the intensive care unit ( ICU), but their diagnosis is difficult. In this study, machine learning was appl ied to design and define the predictive model of ICU-acquired fungi (ICU-AF) in the early stage of fungal infections using Random Forest.” The news reporters obtained a quote from the research from the University of Chi nese Academy of Sciences, “This study aimed to provide evidence for the early wa rning and management of fungal infections. We analyzed the data of patients with culture-positive fungi during their admission to seven ICUs of the First Affili ated Hospital of Chongqing Medical University from January 1, 2015, to December 31, 2019. Patients whose first culture was positive for fungi longer than 48 h a fter ICU admission were included in the ICU-AF cohort. A predictive model of ICU -AF was obtained using the Least Absolute Shrinkage and Selection Operator and m achine learning, and the relationship between the features within the model and the disease severity and mortality of patients was analyzed. Finally, the relati onships between the ICU-AF model, antifungal therapy and empirical antifungal th erapy were analyzed. A total of 1,434 cases were included finally. We used lasso dimensionality reduction for all features and selected six features with import ance 0.05 in the optimal model, namely, times of arterial catheter, enteral nutr ition, corticosteroids, broadspectrum antibiotics, urinary catheter, and invasiv e mechanical ventilation. The area under the curve of the model for predicting I CU-AF was 0.981 in the test set, with a sensitivity of 0.960 and specificity of 0.990. The times of arterial catheter ( = 0.011, OR = 1.057, 95% C I = 1.053-1.104) and invasive mechanical ventilation ( = 0.007, OR = 1.056, 95% CI = 1.015-1.098) were independent risk factors for antifungal therapy in ICU-AF . The times of arterial catheter ( = 0.004, OR = 1.098, 95%CI = 0.8 55- 0.970) were an independent risk factor for empirical antifungal therapy. The most important risk factors for ICU-AF are the six time-related features of clin ical parameters (arterial catheter, enteral nutrition, corticosteroids, broadspe ctrum antibiotics, urinary catheter, and invasive mechanical ventilation), which provide early warning for the occurrence of fungal infection.”
ShenzhenPeople’s Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine LearningRisk and PreventionThera py
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