急性高原病易感性的多指标神经网络预测及应用研究
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摘要
急性高原病（Acute Mountain Sickness，AMS）是指由平原进入高原或由高原进入更高海拔地区，在短期内(数小时至数日)发生的各种临床症候群，分为急性轻型高原病(急性高原反应)、高原肺水肿和高原脑水肿。其发病率高、危害大，是影响快速进入高海拔地区官兵健康和生命安全的主要疾病，也是导致作业能力降低的主要因素。基础研究表明，急性高原病有明显的易感性，但目前尚无预测筛选方法，因此，为了能够给急进高原部队卫勤保障提供可供参考的资源和科学依据，本研究跳出传统思路，突破学科界限，从多因素角度考虑建立AMS易感指标体系，并结合这些指标采用神经网络基本原理和方法建立模型来预测AMS易感的可能性。
一、研究的主要内容
第一、急进高原人群急性高原病易感预测需求研究
第二、急性高原病易感危险因素初步筛选及Meta分析研究
第三、高海拔暴露前易感危险因素与急性高原病关系研究
第四、急性高原病易感神经网络预测模型建立与应用研究
二、研究的主要方法
本课题主要综合运用如下方法进行研究：
第一、调查研究方法：包括文献资料调研，人员基本情况调研和心理症状测评调研，获得数据资料。
第二、系统分析方法：运用系统分析方法，基于循证医学原理初步确立急性高原病易感指标体系。
第三、现场实验方法：通过现场实验，获得指标筛选优化、建立模型和模型验证所需的基本数据。
第四、统计学方法：运用统计分析方法，实现急性高原病易感指标的优化。
第五、模型方法：综合运用复杂系统神经网络方法，实现急性高原病易感者预测判断模型的建立。
三、研究的主要结果
（一）急进高原人群急性高原病易感预测需求研究
采用描述性统计分析、曲线拟合等方法分析了急性高原病疾病减员率与海拔高度的关系。结果表明：进入高海拔人群，在没有剧烈劳动强度情况下，急性高原病疾病减员率仍然较高，在海拔3500米--4000米范围，AMS减员率有95%概率落在2.64--8.83范围内，在4000米以上海拔区域，AMS减员率有95%概率落在9.72--25.34范围内，且随海拔高度增加呈上升趋势。因此，有必要开展高海拔暴露前AMS易感者预测筛选研究，一方面可以降低AMS疾病减员率，另一方面为大部队急进高原卫勤保障提供可供借鉴的科学依据。
（二）急性高原病易感危险因素初步筛选及Meta分析研究
AMS易感危险因素主要集中在生理指标、生化指标、心理指标以及基因指标四个方面，本研究采用文献阅读和Meta分析方法，从大批人群现场实验简易性、无创性、可操作性和安全性角度出发，在考虑主要指标和次要指标基础上，初步确立18项指标用于与AMS发病关系的研究，包括：身高、体重、身体指数、胸廓体积，动脉血氧饱和度、冷刺激后血压变化、血浆皮质醇含量、肺活量、用力肺活量、心率变异、精神因素、屏气时间、血浆ROS水平、血浆去甲肾上腺素、呼出气NO、呼出气CO、吸烟和饮酒。
（三）AMS易感生理、生化、心理健康状态指标的实证研究
在进入高原前1周，应用自制量表和焦虑自评量表、抑郁自评量表、SCL-90症状自评量表对某部314名拟进入高原新兵的基本情况和心理状况进行了调查和测评，通过体格检查获取了18项生理生化指标数据，采用国际通用的Lake Lousie Score急性高原病诊断标准对研究对象进行症状调查与评分。结果表明：新兵在进入高原前存在不同程度的心理应激，焦虑总分、抑郁总分、SCL-90总分与AMS症状分值存在一定相关性，且有焦虑、抑郁、SCL-90阳性症状人员出现AMS症状的比例显著高于无焦虑、抑郁、SCL-90阳性症状人员（2=10.944， p <0.05；2=20.355， p <0.05；2=3.987，
<0.05）。新兵高海拔暴露前FENO、FECO值与AMS存在相关性，FENO和FECO均值在AMS组和非AMS组人群中显著不同（值分别为0.003和小于0.001），在AMS非症状组FENO均值的95%CI为[13.25，16.23]，AMS症状组为[9.07，12.98]；在AMS非症状组FECO均值的95%CI为[5.49，6.72]，AMS症状组为[3.76，5.02]。FECO值与吸烟行为强烈正相关（r=0.831,<0.001），AMS组吸烟率显著低于非AMS组，但是，吸烟者FENO值显著低于不吸烟者（<0.001），因此，虽然FECO值与AMS评分负相关，但是并不意味着FECO值越高，AMS风险越低，我们认为吸烟具有双重作用，在一定范围内，FECO值对AMS的形成具有保护作用。肺活量（VC）与AMS负相关。心率变异、冷激发后血压变化、屏气时间、SaO2、年龄、BMI、饮酒行为与AMS无显著关系（p>0.05），研究对象高海拔暴露前血浆皮质醇含量（Cor值）虽然处于较高水平，但与AMS形成并无显著关系（>0.05）。因此，高海拔暴露前FECO和FENO值也是AMS易感的危险因素，可以作为AMS易感性预测的指标。
（四）急性高原病易感神经网络预测模型建立与应用研究
本研究根据所研究问题的性质和神经网络理论，针对AMS易感指标的特征，采取学习向量量化(LVQ，Learning Vector Quantization)网络和误差反向后传（BackPropagation，BP）网络两种模型，采用比较研究方法、试错法等手段，并通过不断对训练结构和参数的修正，建立了AMS易感预测的LVQ模型，模型的平均正确预测精度达到72.22%，初步实现对AMS易感人群的筛选。因此，LVQ神经网络用于AMS易感预测是可行的，是一种更有前途的AMS易感者筛选方法。
在此基础上，结合研究工作开展情况，提出了LVQ预测模型在急进高原人群AMS易感筛选的应用步骤以及大规模人群急进高原卫勤保障方案。
Acute mountain sickness (AMS) is characterized by the presence of headache and atleast one of the following symptoms: loss of appetite, nausea, vomiting, fatigue/weakness,dizziness/light-headedness, and insomnia. It occurs when people arrive at an altitude above2500m, and can be classified into acute mild high-altitude sickness, high-altitudepulmonary edema, and high-altitude cerebral edema. With a high incidence and greatseverity, AMS is the main threat to the health and life of the serviceman who quicklyascend to high-altitude areas and it is also the main cause for their weakened battle force.Studies have showed that susceptibility to AMS exists evidently，but no feasible method isavailable to predict it at present. Therefore, we try to build a prediction model ofsusceptibility to AMS, by combining multiple indices and the basic principles of neuralnetwork method. In this study, a new approach was adopted, which breaks the boundariesbetween disciplines, in order to provide a scientific basis for medical support for servicemenwho quickly ascend to high altitude.
Main research content
First, the needs analysis of prediction of susceptibility to AMS.
Second, the preliminary screening of the risk factors of susceptibility to AMS andMeta-analysis.
Third, the research on the relationship between the risk factors of susceptibility toAMS and AMS before exposure to high altitude.
Fourth, the research on the building of a neural-network prediction model ofsusceptibility to AMS and its application.
Main research methods
In our study, the following methods were adopted.
First, we collected data by searching and reading through relevant literature,investigating the subjects’ basic demographics, experiences of ascending to high altitude,and drinking and smoking histories, and assessing their mental status or symptoms.
Second, the method of system analysis was used to build a preliminary index system ofsusceptibility to AMS, according to the principles of evidence-based medicine.
Third, field experiments were performed to acquire the data for screening andoptimizing indices, building the model, and verifying the model.
Fourth, statistical methods were adopted to optimize the index system.
Fifth, the neural network method was used to build the prediction model ofsusceptibility to AMS.
Main research results
First，the needs analysis of prediction of susceptibility to AMS.
Using statistical methods including descriptive statistics, curve estimation and so on,the relationship between the rate of personnel losses due to AMS and altitude was identified.The rate of personnel losses due to AMS remained high at high altitudes even with labor ofno intensity, and it was positively correlated with altitude. From3,500m up to4,000mabove see level, the rate of personnel losses was in the range of2.64to8.83, while from4,000m upwards, it was in the range of9.72to25.34. Thus, it is necessary to carry outresearch on susceptibility to AMS of the population who suddenly enter high altitude. Withthis undertaking, it is hopeful to find ways to reduce the incidence of AMS and provide ascientific basis for medical support for the mass population who suddenly enter highaltitude.
Second, the preliminary screening of the risk factors of susceptibility to AMS andMeta-analysis.
The main risk factors of susceptibility to AMS come from four aspects, i.e., physiology,biochemistry, mentality, and gene. Referring to previous studies and applying Meta-analysis,we preliminarily determined18indices to be used for research on susceptibility to AMS,including stature, weight, body mass index (BMI), thoracic cage volume, arterial oxygensaturation (SaO2), blood pressure change after cold stimulation，cortisol of plasma, vitalcapacity (VC), forced vital capacity (FVC), heart rate variability(HRV), mental factors,breathing-holding time, levels of plasma reactive oxygen species, level of plasmanorepinephrine, fraction of exhaled nitric oxide (FENO), fraction of exhaled carbonmonoxide (FECO), smoking and drinking. This selection was also based on facility,maneuverability, and security of experiment on mass population.
Third, the research on the relationship between the risk factors of susceptibility toAMS and AMS before exposure to high altitude.
A total of314healthy young male recruits were voluntarily enrolled. Before thesubjects ascending to the altitude of4,300m, the data concerning their stature, weight, BMI,thoracic cage volume, SaO2, blood pressure change after cold stimulation，blood samples,VC, FVC, HRV, breath-holding time, FENO and FECO values, demographic factors, anddrinking and smoking history were obtained. Mental health scores were also obtained usinga self-rating anxiety scale, a self-rating depression scale and the symptom checklist90(SCL-90). We stayed with the subjects for the first week after reaching the high altitude, toobtain their Lake Louise Score (LLS) on each day of the first week. In the subjects withLLS>4, headache and at least one other symptom were diagnosed as the symptoms of AMS.The highest LLS of each individual during the first7days were considered as the final LLS.The data showed that the recruits had psychological stress before exposure to high altitude.The total score of anxiety symptoms, depression symptoms, and SCL-90was associatedwith AMS score. The AMS incidence of the subjects with anxiety, depression and SCL-90symptoms was significantly higher than that of those without these symptoms（2=10.944，p <0.05；2=20.355， p <0.05；2=3.987，<0.05）. Both FENO and FECO were foundto be significantly associated with AMS. The AMS group had lower FENO(p=0.003) andFECO(p<0.001) values, and a lower smoking rate (p<0.001) than non-AMS group. MeanFENOand FECOvalues was11.03ppb [95%confidence interval (CI)9.07-12.98] and4.39ppm [95%CI3.76-5.02], respectively, in AMS group, and14.74ppb [95%CI13.25-16.23]and6.10ppm [95%CI5.49-6.72], respectively, in non-AMS group (<0.0001). FECOwas strongly correlated with the variables associated with smoking behavior. The AMSgroup had a significantly lower smoking rate than the non-AMS group (p<0.001). However,in the study, the FENO value of smokers was significantly lower than that of non-smokers(p<0.001). Therefore, FECOvalue was negatively correlated with max-LLS and most-dayLLSs, but it did not indicate that the higher FECOvalue is, the lower the risk of AMS wouldbe. We think the effect of smoking is bidirectional. In some range, FECOhas a protective rolefor AMS. VC was negatively correlated with AMS. As for the other factors, such as age,BMI, HVR, thoracic cage volume, blood pressure change after cold stimulation,breathing-holding time, level of cortisol in plasma, and drinking behavior, etc. were not significantly associated with AMS, but the level of cortisol in plasma significantly increased.So, we think that FENO and FECO values before exposure to high altitude were also riskfactors of susceptibility to AMS.
Fourth, the research on the building of a neural-network prediction model ofsusceptibility to AMS and its application
According to the characteristics of AMS susceptibility indices and neural networktheory, we adopted the learning vector quantization (LVQ) and the back propagation (BP)neural network method to build the prediction model of susceptibility to AMS. Using thecomparative method and trial and error method, and with the framework and parameters ofthe network being continually amended, the average correct-prediction precision of theLVQ model ultimately reached72.22%, which can lead to an effective preliminaryscreening of susceptibility to AMS.
Based on the above-mentioned results, we applied the LVQ model to the screening ofAMS susceptibility, according to which the medical support project can be designed for themass population who quickly ascend to high altitude areas.
一、研究的主要内容
第一、急进高原人群急性高原病易感预测需求研究
第二、急性高原病易感危险因素初步筛选及Meta分析研究
第三、高海拔暴露前易感危险因素与急性高原病关系研究
第四、急性高原病易感神经网络预测模型建立与应用研究
二、研究的主要方法
本课题主要综合运用如下方法进行研究：
第一、调查研究方法：包括文献资料调研，人员基本情况调研和心理症状测评调研，获得数据资料。
第二、系统分析方法：运用系统分析方法，基于循证医学原理初步确立急性高原病易感指标体系。
第三、现场实验方法：通过现场实验，获得指标筛选优化、建立模型和模型验证所需的基本数据。
第四、统计学方法：运用统计分析方法，实现急性高原病易感指标的优化。
第五、模型方法：综合运用复杂系统神经网络方法，实现急性高原病易感者预测判断模型的建立。
三、研究的主要结果
（一）急进高原人群急性高原病易感预测需求研究
采用描述性统计分析、曲线拟合等方法分析了急性高原病疾病减员率与海拔高度的关系。结果表明：进入高海拔人群，在没有剧烈劳动强度情况下，急性高原病疾病减员率仍然较高，在海拔3500米--4000米范围，AMS减员率有95%概率落在2.64--8.83范围内，在4000米以上海拔区域，AMS减员率有95%概率落在9.72--25.34范围内，且随海拔高度增加呈上升趋势。因此，有必要开展高海拔暴露前AMS易感者预测筛选研究，一方面可以降低AMS疾病减员率，另一方面为大部队急进高原卫勤保障提供可供借鉴的科学依据。
（二）急性高原病易感危险因素初步筛选及Meta分析研究
AMS易感危险因素主要集中在生理指标、生化指标、心理指标以及基因指标四个方面，本研究采用文献阅读和Meta分析方法，从大批人群现场实验简易性、无创性、可操作性和安全性角度出发，在考虑主要指标和次要指标基础上，初步确立18项指标用于与AMS发病关系的研究，包括：身高、体重、身体指数、胸廓体积，动脉血氧饱和度、冷刺激后血压变化、血浆皮质醇含量、肺活量、用力肺活量、心率变异、精神因素、屏气时间、血浆ROS水平、血浆去甲肾上腺素、呼出气NO、呼出气CO、吸烟和饮酒。
（三）AMS易感生理、生化、心理健康状态指标的实证研究
在进入高原前1周，应用自制量表和焦虑自评量表、抑郁自评量表、SCL-90症状自评量表对某部314名拟进入高原新兵的基本情况和心理状况进行了调查和测评，通过体格检查获取了18项生理生化指标数据，采用国际通用的Lake Lousie Score急性高原病诊断标准对研究对象进行症状调查与评分。结果表明：新兵在进入高原前存在不同程度的心理应激，焦虑总分、抑郁总分、SCL-90总分与AMS症状分值存在一定相关性，且有焦虑、抑郁、SCL-90阳性症状人员出现AMS症状的比例显著高于无焦虑、抑郁、SCL-90阳性症状人员（2=10.944， p <0.05；2=20.355， p <0.05；2=3.987，
<0.05）。新兵高海拔暴露前FENO、FECO值与AMS存在相关性，FENO和FECO均值在AMS组和非AMS组人群中显著不同（值分别为0.003和小于0.001），在AMS非症状组FENO均值的95%CI为[13.25，16.23]，AMS症状组为[9.07，12.98]；在AMS非症状组FECO均值的95%CI为[5.49，6.72]，AMS症状组为[3.76，5.02]。FECO值与吸烟行为强烈正相关（r=0.831,<0.001），AMS组吸烟率显著低于非AMS组，但是，吸烟者FENO值显著低于不吸烟者（<0.001），因此，虽然FECO值与AMS评分负相关，但是并不意味着FECO值越高，AMS风险越低，我们认为吸烟具有双重作用，在一定范围内，FECO值对AMS的形成具有保护作用。肺活量（VC）与AMS负相关。心率变异、冷激发后血压变化、屏气时间、SaO2、年龄、BMI、饮酒行为与AMS无显著关系（p>0.05），研究对象高海拔暴露前血浆皮质醇含量（Cor值）虽然处于较高水平，但与AMS形成并无显著关系（>0.05）。因此，高海拔暴露前FECO和FENO值也是AMS易感的危险因素，可以作为AMS易感性预测的指标。
（四）急性高原病易感神经网络预测模型建立与应用研究
本研究根据所研究问题的性质和神经网络理论，针对AMS易感指标的特征，采取学习向量量化(LVQ，Learning Vector Quantization)网络和误差反向后传（BackPropagation，BP）网络两种模型，采用比较研究方法、试错法等手段，并通过不断对训练结构和参数的修正，建立了AMS易感预测的LVQ模型，模型的平均正确预测精度达到72.22%，初步实现对AMS易感人群的筛选。因此，LVQ神经网络用于AMS易感预测是可行的，是一种更有前途的AMS易感者筛选方法。
在此基础上，结合研究工作开展情况，提出了LVQ预测模型在急进高原人群AMS易感筛选的应用步骤以及大规模人群急进高原卫勤保障方案。
Acute mountain sickness (AMS) is characterized by the presence of headache and atleast one of the following symptoms: loss of appetite, nausea, vomiting, fatigue/weakness,dizziness/light-headedness, and insomnia. It occurs when people arrive at an altitude above2500m, and can be classified into acute mild high-altitude sickness, high-altitudepulmonary edema, and high-altitude cerebral edema. With a high incidence and greatseverity, AMS is the main threat to the health and life of the serviceman who quicklyascend to high-altitude areas and it is also the main cause for their weakened battle force.Studies have showed that susceptibility to AMS exists evidently，but no feasible method isavailable to predict it at present. Therefore, we try to build a prediction model ofsusceptibility to AMS, by combining multiple indices and the basic principles of neuralnetwork method. In this study, a new approach was adopted, which breaks the boundariesbetween disciplines, in order to provide a scientific basis for medical support for servicemenwho quickly ascend to high altitude.
Main research content
First, the needs analysis of prediction of susceptibility to AMS.
Second, the preliminary screening of the risk factors of susceptibility to AMS andMeta-analysis.
Third, the research on the relationship between the risk factors of susceptibility toAMS and AMS before exposure to high altitude.
Fourth, the research on the building of a neural-network prediction model ofsusceptibility to AMS and its application.
Main research methods
In our study, the following methods were adopted.
First, we collected data by searching and reading through relevant literature,investigating the subjects’ basic demographics, experiences of ascending to high altitude,and drinking and smoking histories, and assessing their mental status or symptoms.
Second, the method of system analysis was used to build a preliminary index system ofsusceptibility to AMS, according to the principles of evidence-based medicine.
Third, field experiments were performed to acquire the data for screening andoptimizing indices, building the model, and verifying the model.
Fourth, statistical methods were adopted to optimize the index system.
Fifth, the neural network method was used to build the prediction model ofsusceptibility to AMS.
Main research results
First，the needs analysis of prediction of susceptibility to AMS.
Using statistical methods including descriptive statistics, curve estimation and so on,the relationship between the rate of personnel losses due to AMS and altitude was identified.The rate of personnel losses due to AMS remained high at high altitudes even with labor ofno intensity, and it was positively correlated with altitude. From3,500m up to4,000mabove see level, the rate of personnel losses was in the range of2.64to8.83, while from4,000m upwards, it was in the range of9.72to25.34. Thus, it is necessary to carry outresearch on susceptibility to AMS of the population who suddenly enter high altitude. Withthis undertaking, it is hopeful to find ways to reduce the incidence of AMS and provide ascientific basis for medical support for the mass population who suddenly enter highaltitude.
Second, the preliminary screening of the risk factors of susceptibility to AMS andMeta-analysis.
The main risk factors of susceptibility to AMS come from four aspects, i.e., physiology,biochemistry, mentality, and gene. Referring to previous studies and applying Meta-analysis,we preliminarily determined18indices to be used for research on susceptibility to AMS,including stature, weight, body mass index (BMI), thoracic cage volume, arterial oxygensaturation (SaO2), blood pressure change after cold stimulation，cortisol of plasma, vitalcapacity (VC), forced vital capacity (FVC), heart rate variability(HRV), mental factors,breathing-holding time, levels of plasma reactive oxygen species, level of plasmanorepinephrine, fraction of exhaled nitric oxide (FENO), fraction of exhaled carbonmonoxide (FECO), smoking and drinking. This selection was also based on facility,maneuverability, and security of experiment on mass population.
Third, the research on the relationship between the risk factors of susceptibility toAMS and AMS before exposure to high altitude.
A total of314healthy young male recruits were voluntarily enrolled. Before thesubjects ascending to the altitude of4,300m, the data concerning their stature, weight, BMI,thoracic cage volume, SaO2, blood pressure change after cold stimulation，blood samples,VC, FVC, HRV, breath-holding time, FENO and FECO values, demographic factors, anddrinking and smoking history were obtained. Mental health scores were also obtained usinga self-rating anxiety scale, a self-rating depression scale and the symptom checklist90(SCL-90). We stayed with the subjects for the first week after reaching the high altitude, toobtain their Lake Louise Score (LLS) on each day of the first week. In the subjects withLLS>4, headache and at least one other symptom were diagnosed as the symptoms of AMS.The highest LLS of each individual during the first7days were considered as the final LLS.The data showed that the recruits had psychological stress before exposure to high altitude.The total score of anxiety symptoms, depression symptoms, and SCL-90was associatedwith AMS score. The AMS incidence of the subjects with anxiety, depression and SCL-90symptoms was significantly higher than that of those without these symptoms（2=10.944，p <0.05；2=20.355， p <0.05；2=3.987，<0.05）. Both FENO and FECO were foundto be significantly associated with AMS. The AMS group had lower FENO(p=0.003) andFECO(p<0.001) values, and a lower smoking rate (p<0.001) than non-AMS group. MeanFENOand FECOvalues was11.03ppb [95%confidence interval (CI)9.07-12.98] and4.39ppm [95%CI3.76-5.02], respectively, in AMS group, and14.74ppb [95%CI13.25-16.23]and6.10ppm [95%CI5.49-6.72], respectively, in non-AMS group (<0.0001). FECOwas strongly correlated with the variables associated with smoking behavior. The AMSgroup had a significantly lower smoking rate than the non-AMS group (p<0.001). However,in the study, the FENO value of smokers was significantly lower than that of non-smokers(p<0.001). Therefore, FECOvalue was negatively correlated with max-LLS and most-dayLLSs, but it did not indicate that the higher FECOvalue is, the lower the risk of AMS wouldbe. We think the effect of smoking is bidirectional. In some range, FECOhas a protective rolefor AMS. VC was negatively correlated with AMS. As for the other factors, such as age,BMI, HVR, thoracic cage volume, blood pressure change after cold stimulation,breathing-holding time, level of cortisol in plasma, and drinking behavior, etc. were not significantly associated with AMS, but the level of cortisol in plasma significantly increased.So, we think that FENO and FECO values before exposure to high altitude were also riskfactors of susceptibility to AMS.
Fourth, the research on the building of a neural-network prediction model ofsusceptibility to AMS and its application
According to the characteristics of AMS susceptibility indices and neural networktheory, we adopted the learning vector quantization (LVQ) and the back propagation (BP)neural network method to build the prediction model of susceptibility to AMS. Using thecomparative method and trial and error method, and with the framework and parameters ofthe network being continually amended, the average correct-prediction precision of theLVQ model ultimately reached72.22%, which can lead to an effective preliminaryscreening of susceptibility to AMS.
Based on the above-mentioned results, we applied the LVQ model to the screening ofAMS susceptibility, according to which the medical support project can be designed for themass population who quickly ascend to high altitude areas.
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