目的探讨血小板压积(plateletcrit,PCT)联合收缩压(systolic blood pressure,SBP)和急性生理与慢性健康评分(Acute Physiology and Chronic Health Evaluation,APACHEⅡ评分)对脓毒性休克的预测价值。方法入选2018年1月~2021年12月山西...目的探讨血小板压积(plateletcrit,PCT)联合收缩压(systolic blood pressure,SBP)和急性生理与慢性健康评分(Acute Physiology and Chronic Health Evaluation,APACHEⅡ评分)对脓毒性休克的预测价值。方法入选2018年1月~2021年12月山西省人民医院收治的131例脓毒症患者作为研究对象,记录患者基线资料和临床数据。根据是否发生脓毒性休克,将131例患者分为脓毒症组(n=68)和脓毒性休克组(n=63)。比较两组临床资料,采用二元Logistic回归模型分析发生脓毒性休克的独立危险因素。采用受试者工作特征(receiver operating characteristic,ROC)曲线评价PCT、SBP和APACHEⅡ评分及三者联合对脓毒性休克的预测价值。结果两组患者年龄、性别、C反应蛋白、血小板分布宽度和白细胞计数等比较,差异无统计学意义(P>0.05)。与脓毒症组比较,脓毒性休克组收缩压、舒张压、血小板计数、血小板压积和嗜酸性粒细胞计数显著降低;心率、D二聚体、降钙素原、序贯器官衰竭评估(sequential organ failure assessment,SOFA)评分和APACHEⅡ评分升高,差异有统计学意义(P<0.05)。Logistic回归分析显示,低PCT、低SBP和APACHEⅡ评分是脓毒症休克的独立危险因素。ROC曲线分析显示,PCT、SBP和APACHEⅡ预测脓毒症发生的曲线下面积(area under the curve,AUC)分别为0.653、0.665和0.692,而三者联合后,曲线下面积为0.794。结论血小板压积可作为预测脓毒性休克的指标,与收缩压及APACHEⅡ评分联合能够提高预测脓毒性休克的准确性。展开更多
Big data analytics is a popular research topic due to its applicability in various real time applications.The recent advent of machine learning and deep learning models can be applied to analyze big data with better p...Big data analytics is a popular research topic due to its applicability in various real time applications.The recent advent of machine learning and deep learning models can be applied to analyze big data with better performance.Since big data involves numerous features and necessitates high computational time,feature selection methodologies using metaheuristic optimization algorithms can be adopted to choose optimum set of features and thereby improves the overall classification performance.This study proposes a new sigmoid butterfly optimization method with an optimum gated recurrent unit(SBOA-OGRU)model for big data classification in Apache Spark.The SBOA-OGRU technique involves the design of SBOA based feature selection technique to choose an optimum subset of features.In addition,OGRU based classification model is employed to classify the big data into appropriate classes.Besides,the hyperparameter tuning of the GRU model takes place using Adam optimizer.Furthermore,the Apache Spark platform is applied for processing big data in an effective way.In order to ensure the betterment of the SBOA-OGRU technique,a wide range of experiments were performed and the experimental results highlighted the supremacy of the SBOA-OGRU technique.展开更多
文摘目的探讨血小板压积(plateletcrit,PCT)联合收缩压(systolic blood pressure,SBP)和急性生理与慢性健康评分(Acute Physiology and Chronic Health Evaluation,APACHEⅡ评分)对脓毒性休克的预测价值。方法入选2018年1月~2021年12月山西省人民医院收治的131例脓毒症患者作为研究对象,记录患者基线资料和临床数据。根据是否发生脓毒性休克,将131例患者分为脓毒症组(n=68)和脓毒性休克组(n=63)。比较两组临床资料,采用二元Logistic回归模型分析发生脓毒性休克的独立危险因素。采用受试者工作特征(receiver operating characteristic,ROC)曲线评价PCT、SBP和APACHEⅡ评分及三者联合对脓毒性休克的预测价值。结果两组患者年龄、性别、C反应蛋白、血小板分布宽度和白细胞计数等比较,差异无统计学意义(P>0.05)。与脓毒症组比较,脓毒性休克组收缩压、舒张压、血小板计数、血小板压积和嗜酸性粒细胞计数显著降低;心率、D二聚体、降钙素原、序贯器官衰竭评估(sequential organ failure assessment,SOFA)评分和APACHEⅡ评分升高,差异有统计学意义(P<0.05)。Logistic回归分析显示,低PCT、低SBP和APACHEⅡ评分是脓毒症休克的独立危险因素。ROC曲线分析显示,PCT、SBP和APACHEⅡ预测脓毒症发生的曲线下面积(area under the curve,AUC)分别为0.653、0.665和0.692,而三者联合后,曲线下面积为0.794。结论血小板压积可作为预测脓毒性休克的指标,与收缩压及APACHEⅡ评分联合能够提高预测脓毒性休克的准确性。
文摘Big data analytics is a popular research topic due to its applicability in various real time applications.The recent advent of machine learning and deep learning models can be applied to analyze big data with better performance.Since big data involves numerous features and necessitates high computational time,feature selection methodologies using metaheuristic optimization algorithms can be adopted to choose optimum set of features and thereby improves the overall classification performance.This study proposes a new sigmoid butterfly optimization method with an optimum gated recurrent unit(SBOA-OGRU)model for big data classification in Apache Spark.The SBOA-OGRU technique involves the design of SBOA based feature selection technique to choose an optimum subset of features.In addition,OGRU based classification model is employed to classify the big data into appropriate classes.Besides,the hyperparameter tuning of the GRU model takes place using Adam optimizer.Furthermore,the Apache Spark platform is applied for processing big data in an effective way.In order to ensure the betterment of the SBOA-OGRU technique,a wide range of experiments were performed and the experimental results highlighted the supremacy of the SBOA-OGRU technique.
