摘要
机器人与机器学习的新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。根据NewsRx记者对深圳的新闻报道，研究表明：“在重症监护病房(ICU)中，真菌感染与高发病率和高死亡率有关，但诊断困难，本研究应用机器学习方法设计并定义了真菌感染早期ICU获得真菌(ICU-AF)的预测模型。”本文对重庆医科大学第一附属医院7个重症监护病房2015年1月1日至12月31日收治的培养阳性真菌患者的临床资料进行了分析。2019年。ICU-AF队列纳入ICU-AF首次培养时间超过48h的患者，采用最小绝对收缩选择算子和机器学习建立ICU-AF预测模型，分析模型内特征与患者疾病严重程度和死亡率的关系，最后分析ICU-AF模型与ICU-AF模型之间的相关性。对1434例抗真菌治疗和经验性抗真菌治疗进行分析,采用Lasso降维方法对所有特征进行降维处理,在最优模型中筛选出6个重要的特征,即动脉导管次数、肠内营养、皮质类固醇激素、广谱抗生素、导尿管次数、排尿次数模型预测I Cu-AF的曲线下面积为0.981,对动脉导管次数(=0.011,OR=1.057,95%Ci=1.053～1.104)和有创机械通气次数(=0.007,OR=1.056,敏感性0.960,特异性0.990.。95%CI=1.015-1.098)是ICU-AF抗真菌治疗的独立危险因素,动脉导管次数(=0.004,OR=1.098,95%CI=0.855-0.970)是ICU-AF经验性抗真菌治疗的独立危险因素,最重要的危险因素是临床参数(动脉导管、肠内营养、皮质类固醇、广谱抗生素、导尿管和有创机械通气)的时间相关性。为真菌感染的发生提供早期预警。
Abstract
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.”
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