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
目的:探讨优化个体护理用于重症监护室(ICU)重症肺炎患者对其急性生理与慢性健康评分(AcutePhysiology and Chronic Health Evaluation,APACHE Ⅱ)的影响。方法:随机选取本院收治的106例重症肺炎患者,抽样时间为2020年1月至2021年12月,...目的:探讨优化个体护理用于重症监护室(ICU)重症肺炎患者对其急性生理与慢性健康评分(AcutePhysiology and Chronic Health Evaluation,APACHE Ⅱ)的影响。方法:随机选取本院收治的106例重症肺炎患者,抽样时间为2020年1月至2021年12月,采用随机数表法将其分为观察组和对照组,每组各53例。对照组实施常规护理干预,观察组采用优化个体护理干预。观察两组护理前后的APACHE Ⅱ评分,对比两组患者护理前后血清乳酸水平(LCA)、C反应蛋白(CRP)、D-二聚体(D-D)以及氧合指数(OI)水平,记录两组患者的并发症情况及死亡率。结果:护理第1、3、5、7天,两组患者的APACHE Ⅱ评分均明显降低,观察组的APACHE Ⅱ评分均显著低于对照组,差异有统计学意义(P<0.05)。护理前,两组患者各项生理指标相比,差异无统计学意义(P>0.05);护理后,观察组的LCA、CRP、D-D、OI水平均明显低于对照组,差异有统计学意义(P<0.05)。观察组患者并发症总发生率为1.89%、死亡率为1.89%,明显低于对照组并发症总发生率14.09%、死亡率13.21%,差异有统计学意义(P<0.05)。结论:优化个体护理在ICU重症肺炎患者护理中发挥着积极作用,不仅能改善患者APACHE Ⅱ评分,取得更好的预后效果,更能降低病死率,值得临床推广使用。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.
文摘目的:探讨优化个体护理用于重症监护室(ICU)重症肺炎患者对其急性生理与慢性健康评分(AcutePhysiology and Chronic Health Evaluation,APACHE Ⅱ)的影响。方法:随机选取本院收治的106例重症肺炎患者,抽样时间为2020年1月至2021年12月,采用随机数表法将其分为观察组和对照组,每组各53例。对照组实施常规护理干预,观察组采用优化个体护理干预。观察两组护理前后的APACHE Ⅱ评分,对比两组患者护理前后血清乳酸水平(LCA)、C反应蛋白(CRP)、D-二聚体(D-D)以及氧合指数(OI)水平,记录两组患者的并发症情况及死亡率。结果:护理第1、3、5、7天,两组患者的APACHE Ⅱ评分均明显降低,观察组的APACHE Ⅱ评分均显著低于对照组,差异有统计学意义(P<0.05)。护理前,两组患者各项生理指标相比,差异无统计学意义(P>0.05);护理后,观察组的LCA、CRP、D-D、OI水平均明显低于对照组,差异有统计学意义(P<0.05)。观察组患者并发症总发生率为1.89%、死亡率为1.89%,明显低于对照组并发症总发生率14.09%、死亡率13.21%,差异有统计学意义(P<0.05)。结论:优化个体护理在ICU重症肺炎患者护理中发挥着积极作用,不仅能改善患者APACHE Ⅱ评分,取得更好的预后效果,更能降低病死率,值得临床推广使用。
