目的探讨食物交换份手测量饮食教育在居家老年慢性心力衰竭患者中的应用效果。方法老年慢性心力衰竭患者90例随机分为对照组和试验组,每组45例。2组患者均利用科室自制的心脏康复手册,由康复护士针对手册中的营养篇内容进行饮食健康教育...目的探讨食物交换份手测量饮食教育在居家老年慢性心力衰竭患者中的应用效果。方法老年慢性心力衰竭患者90例随机分为对照组和试验组,每组45例。2组患者均利用科室自制的心脏康复手册,由康复护士针对手册中的营养篇内容进行饮食健康教育,试验组患者在此基础上进行专人指导的食物交换份手测量饮食训练,出院时和出院2月后接受人体成分分析评估营养状况,6 min步行试验(6-minute walk test,6MWT)评估心肺功能,对比2组患者的干预效果。结果研究结束试验组患者去脂体重、蛋白质质量较干预前及对照组有明显升高,差异有统计学意义(P<0.05);体脂肪、体脂率、内脏脂肪面积较干预前及对照组明显降低,差异有统计学意义(P<0.05),对照组去脂体重、蛋白质质量有所升高,体脂肪、体脂率、内脏脂肪面积有所降低,但与干预前比较差异无统计学意义(P>0.05)。结论食物交换份手测量饮食教育能有效改善居家老年慢性心力衰竭患者的营养状况,并提高其心肺功能和运动耐力,可尝试用于老年慢性心力衰竭患者的出院健康指导。展开更多
<div style="text-align:justify;"> Recent days, heart ailments assume a fundamental role in the world. The physician gives different name for heart disease, for example, cardiovascular failure, heart fa...<div style="text-align:justify;"> Recent days, heart ailments assume a fundamental role in the world. The physician gives different name for heart disease, for example, cardiovascular failure, heart failure and so on. Among the automated techniques to discover the coronary illness, this research work uses Named Entity Recognition (NER) algorithm to discover the equivalent words for the coronary illness content to mine the significance in clinical reports and different applications. The Heart sickness text information given by the physician is taken for the preprocessing and changes the text information to the ideal meaning, at that point the resultant text data taken as input for the prediction of heart disease. This experimental work utilizes the NER to discover the equivalent words of the coronary illness text data and currently uses the two strategies namely Optimal Deep Learning and Whale Optimization which are consolidated and proposed another strategy Optimal Deep Neural Network (ODNN) for predicting the illness. For the prediction, weights and ranges of the patient affected information by means of chosen attributes are picked for the experiment. The outcome is then characterized with the Deep Neural Network and Artificial Neural Network to discover the accuracy of the algorithms. The performance of the ODNN is assessed by means for classification methods, for example, precision, recall and f-measure values. </div>展开更多
文摘目的探讨食物交换份手测量饮食教育在居家老年慢性心力衰竭患者中的应用效果。方法老年慢性心力衰竭患者90例随机分为对照组和试验组,每组45例。2组患者均利用科室自制的心脏康复手册,由康复护士针对手册中的营养篇内容进行饮食健康教育,试验组患者在此基础上进行专人指导的食物交换份手测量饮食训练,出院时和出院2月后接受人体成分分析评估营养状况,6 min步行试验(6-minute walk test,6MWT)评估心肺功能,对比2组患者的干预效果。结果研究结束试验组患者去脂体重、蛋白质质量较干预前及对照组有明显升高,差异有统计学意义(P<0.05);体脂肪、体脂率、内脏脂肪面积较干预前及对照组明显降低,差异有统计学意义(P<0.05),对照组去脂体重、蛋白质质量有所升高,体脂肪、体脂率、内脏脂肪面积有所降低,但与干预前比较差异无统计学意义(P>0.05)。结论食物交换份手测量饮食教育能有效改善居家老年慢性心力衰竭患者的营养状况,并提高其心肺功能和运动耐力,可尝试用于老年慢性心力衰竭患者的出院健康指导。
文摘<div style="text-align:justify;"> Recent days, heart ailments assume a fundamental role in the world. The physician gives different name for heart disease, for example, cardiovascular failure, heart failure and so on. Among the automated techniques to discover the coronary illness, this research work uses Named Entity Recognition (NER) algorithm to discover the equivalent words for the coronary illness content to mine the significance in clinical reports and different applications. The Heart sickness text information given by the physician is taken for the preprocessing and changes the text information to the ideal meaning, at that point the resultant text data taken as input for the prediction of heart disease. This experimental work utilizes the NER to discover the equivalent words of the coronary illness text data and currently uses the two strategies namely Optimal Deep Learning and Whale Optimization which are consolidated and proposed another strategy Optimal Deep Neural Network (ODNN) for predicting the illness. For the prediction, weights and ranges of the patient affected information by means of chosen attributes are picked for the experiment. The outcome is then characterized with the Deep Neural Network and Artificial Neural Network to discover the accuracy of the algorithms. The performance of the ODNN is assessed by means for classification methods, for example, precision, recall and f-measure values. </div>
