一秒率、残总百分比及功能残气量参考值的地理分布

发布时间：2018-05-24 04:18

本文选题：医学指标参考值 + 地理因素　；参考：《陕西师范大学》2010年硕士论文

【摘要】： 目的：一秒率、残总百分比和功能残气量参考值是肺功能检查的重要指标,常用于判断气流阻塞的程度,为疾病的诊断和防治提供依据。由于国内外缺乏一秒率、残总百分比和功能残气量参考值的统一标准,严重影响了临床诊断的准确性。许多医学工作者只是测定了某些地区的一秒率、残总百分比和功能残气量参考值,有的只是简单的描述了一秒率、残总百分比和功能残气量参考值与地理因素的定性的关系,对于它们之间的专题化,定量化研究未见报道过。方法：本文选取有关地理文献的六项地理指标,即为海拔高度、年日照时数、年平均气温、气温年较差、年平均相对湿度、年降水量作为影响因素进行研究。探讨了不同年龄、不同性别中国人的一秒率、残总百分比和功能残气量参考值与其六项地理因素之间的关系。并以整个中国为研究区域,借助GIS中的地统计方法绘制出医学指标参考值的地理分布图。结果：本文利用因子分析的方法研究了老年前期男性一秒率参考值、中年男性残总百分比参考值、老年男性残总百分比参考值与地理因素的关系,建立了数学模型,同时利用数学模型预测出未知点的医学指标参考值。老年前期男性一秒率参考值与地理因素之间的因子分析回归模型为：Y=77.93-0.0004518X1-0.0005240X2+0.05639X3-0.01615X4+0.03800X5+0.0003352X6±4.333 中年男性残总百分比参考值与地理因素之间的因子分析回归模型为：Y=28.49-0.0007474x1-0.0002139x2+0.04396x3+0.03615x4+0.02961x5+0.0005054x6±4.552 老年男性残总百分比参考值与地理因素之间的因子分析回归模型为：Y=33.49-0.001319x1-0.0003431x2+0.09287x3-0.01044x4+0.04255x5 +0.001286x6±4.856 通过建立BP人工神经网络,选取对医学指标参考值影响比较大的地理因素,研究了中年男性一秒率参考值、中年女性一秒率参考值、中年女性残总百分比参考值、中年功能残气量与地理因素的关系。研究中年男性一秒率参考值与地理因素的关系,选取年日照时数、年平均气温、气温年较差、年相对湿度及年降水量为输入数据,构建BP人工神经网络,训练次数为1500次时效果最好。研究中年女性一秒率参考值与地理因素的关系,选取日照时数、年平均气温、气温年较差、年相对湿度、年降水量为输入数据,构建BP人工神经网络,训练次数为1200次时效果最好。研究中年女性残总百分比参考值与地理因素的关系,选取海拔高度、年日照时数、年平均气温、年相对湿度为输入数据,构建BP人工神经网络,训练次数为1500次时效果最好。研究中年功能残气量与地理因素的关系,选取年日照时数、年平均气温、气温年较差、年相对湿度、年降水量为输入数据,构建BP人工神经网络,训练次数为700次时效果最好。结论：以全国作为研究区域,选取全国4383个地区作为基础,利用数学模型或建立BP人工神经网络获得未知点的医学指标参考值,借助于GIS地统计方法,绘制出医学指标参考值的地理分布图。因此,如果知道了某一地区的地理因素值就可以通过数学模型或者建立BP人工神经网络获得这一地区的医学指标参考值,也可以通过查询医学指标参考值地理分布图获得。
[Abstract]:Objective : The reference value of one - second rate , residual total percentage and functional residual air quantity is an important index of pulmonary function examination , which is often used to judge the degree of obstruction of airflow and provide a basis for the diagnosis and prevention of diseases .

Results : In this paper , the relationship between the reference value , the reference value of the total percentage of male residue in middle age , the reference value of the total percentage of male residue in the elderly and the geographical factors were studied by means of factor analysis . The mathematical model was established , and the reference value of medical index of unknown point was predicted by mathematical model .

The regression model of factor analysis between the reference value and geographical factors for male one second rate in the early age is Y = 77.93 - 0.000004518X1 - 0.000005240X2 + 0.05639X3 - 0.01615X4 + 0.03800X5 + 0.00332X6 卤 4.333

The regression model of factor analysis between the reference value of male residual percentage and geographical factors in middle age is : Y = 28.49 - 0.000007474x1 - 0.000002139x2 + 0.04396x3 + 0.03615x4 + 0.02961x + 0.000554x6 卤 4.552

The regression model of factor analysis between the reference value and geographical factors for male residual percentage in the elderly is : Y = 33.49 - 0.001319x1 - 0.000003431x2 + 0.09287x3 - 0.01044x4 + 0.04255x5 + 0.001286x6 卤 4.856

By establishing the BP artificial neural network and selecting the geographical factors which influence the reference value of medical index , the relation between the reference value of the first - second rate of middle - aged man , the reference value of middle - aged female , the reference value of female residual percentage in middle age , the reference value of female residual percentage in middle age , and the geographical factors of middle - aged functional residual capacity is studied .

The relationship between the reference value and geographical factors in the middle - aged man was studied , the annual sunshine duration , the annual average temperature , the annual average temperature , the annual relative humidity and the annual precipitation were selected as input data , the BP artificial neural network was constructed , and the training frequency was 1500 times .

In this paper , the relationship between the reference value and geographical factors of the first - second rate of female in middle - aged women was studied , the sunshine hours , annual average temperature , annual temperature , annual relative humidity and annual precipitation were selected as input data , BP artificial neural network was constructed , and the training frequency was 1200 times .

In this paper , the relationship between the reference value of total percentage of female residue and geographical factors in middle - aged women was studied . The data of altitude , annual sunshine duration , annual average temperature and annual relative humidity were selected as input data . BP artificial neural network was constructed , and the training frequency was 1500 times .

The relationship between the function residual capacity and geographical factors in middle - aged and middle - aged is studied . The annual sunshine duration , annual average temperature , annual temperature difference , annual relative humidity and annual precipitation amount are input data , BP artificial neural network is constructed , and the training frequency is 700 times , and the effect is best .

Conclusion : Using a mathematical model or establishing BP artificial neural network to obtain the reference value of medical index of unknown points , the geographical distribution map of medical index reference value can be obtained by using mathematical model or BP artificial neural network . Therefore , if the geographical factor value of a region is known , the reference value of medical index can be obtained through mathematical model or BP artificial neural network .
【学位授予单位】：陕西师范大学
【学位级别】：硕士
【学位授予年份】：2010
【分类号】：R188

【参考文献】