目的·探讨基于EMS[环境管理(environment management,E)、用药指导(medicine direction,M)与自我监测(self monitoring,S)]管理模式的延续性护理在学龄前喘息性疾病儿童中的应用效果。方法·选取2019年12月至2020年11月,在上...目的·探讨基于EMS[环境管理(environment management,E)、用药指导(medicine direction,M)与自我监测(self monitoring,S)]管理模式的延续性护理在学龄前喘息性疾病儿童中的应用效果。方法·选取2019年12月至2020年11月,在上海交通大学医学院附属儿童医院呼吸科收治的67例0~6岁喘息性疾病患儿,按照随机数字表分为观察组33例和对照组34例,其中失访3例,最终每组32例。观察组采用基于EMS管理模式的延续性护理,对照组给予常规护理和出院电话随访。2组患儿出院后1、3、6个月随访评估儿童呼吸和哮喘测试(Test for Respiratory and Asthma Control in Kids,TRACK)结果、喘息复发情况;出院后6个月随访采用支气管哮喘用药依从性评分表(Medication Adherence Report Scale for Asthma,MARS-A)和护理工作满意度调查表评估用药依从性及护理工作满意度。结果·2组患儿人口学特征及临床基线特征差异无统计学意义。重复测量方差分析结果显示,时间、组别、组别×时间的交互作用对TRACK总分的影响均有统计学意义;出院后1、3、6个月,观察组TRACK总分均显著高于对照组(均P=0.000);2组患儿TRACK总分均随时间推移逐渐上升(P=0.000)。观察组1、3、6个月随访发现喘息复发率分别为25.0%、18.7%、9.4%,均显著低于对照组(分别为50.0%、43.7%、31.3%,均P<0.05);广义估计方程分析显示组间比较差异有统计学意义(P=0.013),观察组干预效果优于对照组(OR=0.292)。出院后6个月观察组MARS-A得分为(4.519±0.395)分,显著高于对照组[(3.994±0.739)分,P=0.001]。护理工作满意度调查结果显示,观察组显著高于对照组(P=0.000)。患儿MARS-A得分与护理工作满意度呈中度正相关(r=0.389,P=0.001)。结论·基于EMS管理模式的延续性护理可显著提高学龄前喘息性疾病儿童的用药依从性和喘息控制水平,明显降低喘息复发率,以及提高护理工作满意度。展开更多
A novel method under the interactive multiple model (IMM) filtering framework is presented in this paper, in which the expectation-maximization (EM) algorithm is used to identify the process noise covariance Q online....A novel method under the interactive multiple model (IMM) filtering framework is presented in this paper, in which the expectation-maximization (EM) algorithm is used to identify the process noise covariance Q online. For the existing IMM filtering theory, the matrix Q is determined by means of design experience, but Q is actually changed with the state of the maneuvering target. Meanwhile it is severely influenced by the environment around the target, i.e., it is a variable of time. Therefore, the experiential covariance Q can not represent the influence of state noise in the maneuvering process exactly. Firstly, it is assumed that the evolved state and the initial conditions of the system can be modeled by using Gaussian distribution, although the dynamic system is of a nonlinear measurement equation, and furthermore the EM algorithm based on IMM filtering with the Q identification online is proposed. Secondly, the truncated error analysis is performed. Finally, the Monte Carlo simulation results are given to show that the proposed algorithm outperforms the existing algorithms and the tracking precision for the maneuvering targets is improved efficiently.展开更多
文摘目的·探讨基于EMS[环境管理(environment management,E)、用药指导(medicine direction,M)与自我监测(self monitoring,S)]管理模式的延续性护理在学龄前喘息性疾病儿童中的应用效果。方法·选取2019年12月至2020年11月,在上海交通大学医学院附属儿童医院呼吸科收治的67例0~6岁喘息性疾病患儿,按照随机数字表分为观察组33例和对照组34例,其中失访3例,最终每组32例。观察组采用基于EMS管理模式的延续性护理,对照组给予常规护理和出院电话随访。2组患儿出院后1、3、6个月随访评估儿童呼吸和哮喘测试(Test for Respiratory and Asthma Control in Kids,TRACK)结果、喘息复发情况;出院后6个月随访采用支气管哮喘用药依从性评分表(Medication Adherence Report Scale for Asthma,MARS-A)和护理工作满意度调查表评估用药依从性及护理工作满意度。结果·2组患儿人口学特征及临床基线特征差异无统计学意义。重复测量方差分析结果显示,时间、组别、组别×时间的交互作用对TRACK总分的影响均有统计学意义;出院后1、3、6个月,观察组TRACK总分均显著高于对照组(均P=0.000);2组患儿TRACK总分均随时间推移逐渐上升(P=0.000)。观察组1、3、6个月随访发现喘息复发率分别为25.0%、18.7%、9.4%,均显著低于对照组(分别为50.0%、43.7%、31.3%,均P<0.05);广义估计方程分析显示组间比较差异有统计学意义(P=0.013),观察组干预效果优于对照组(OR=0.292)。出院后6个月观察组MARS-A得分为(4.519±0.395)分,显著高于对照组[(3.994±0.739)分,P=0.001]。护理工作满意度调查结果显示,观察组显著高于对照组(P=0.000)。患儿MARS-A得分与护理工作满意度呈中度正相关(r=0.389,P=0.001)。结论·基于EMS管理模式的延续性护理可显著提高学龄前喘息性疾病儿童的用药依从性和喘息控制水平,明显降低喘息复发率,以及提高护理工作满意度。
基金Supported by the National Key Fundamental Research & Development Programs of P. R. China (2001CB309403)
文摘A novel method under the interactive multiple model (IMM) filtering framework is presented in this paper, in which the expectation-maximization (EM) algorithm is used to identify the process noise covariance Q online. For the existing IMM filtering theory, the matrix Q is determined by means of design experience, but Q is actually changed with the state of the maneuvering target. Meanwhile it is severely influenced by the environment around the target, i.e., it is a variable of time. Therefore, the experiential covariance Q can not represent the influence of state noise in the maneuvering process exactly. Firstly, it is assumed that the evolved state and the initial conditions of the system can be modeled by using Gaussian distribution, although the dynamic system is of a nonlinear measurement equation, and furthermore the EM algorithm based on IMM filtering with the Q identification online is proposed. Secondly, the truncated error analysis is performed. Finally, the Monte Carlo simulation results are given to show that the proposed algorithm outperforms the existing algorithms and the tracking precision for the maneuvering targets is improved efficiently.