貝葉斯網(wǎng)絡(luò)模型在老年人認(rèn)知功能評(píng)價(jià)隊(duì)列研究中的應(yīng)用
發(fā)布時(shí)間：2018-07-16 08:05
【摘要】：目的:基于阿爾茨海默病(Alzheimer disease,AD)的疾病自然史,將貝葉斯網(wǎng)絡(luò)模型應(yīng)用于老年人認(rèn)知評(píng)價(jià)研究,建立老年人認(rèn)知功能評(píng)價(jià)的推理及預(yù)測(cè)模型,探討影響老年人認(rèn)知功能的因素,為制定AD不同發(fā)展階段的防治措施提供理論依據(jù)。同時(shí)從實(shí)際應(yīng)用角度出發(fā),挖掘貝葉斯統(tǒng)計(jì)在隊(duì)列研究數(shù)據(jù)分析中的優(yōu)勢(shì),為其他慢性病的進(jìn)程研究提供方法學(xué)借鑒,將精準(zhǔn)醫(yī)學(xué)的概念擴(kuò)展至疾病風(fēng)險(xiǎn)預(yù)測(cè)。方法:采用項(xiàng)目組前期的調(diào)查隨訪數(shù)據(jù),以2014年5月的數(shù)據(jù)為基線數(shù)據(jù),對(duì)301名調(diào)查對(duì)象以正常老化→輕度認(rèn)知損害(Mild Cognitive Impairment,MCI)、MCI→中重度認(rèn)知損害、中重度認(rèn)知損害→AD是否轉(zhuǎn)移構(gòu)建Logistic回歸模型,篩選對(duì)認(rèn)知退化有影響的因素,將進(jìn)入模型的所有變量構(gòu)建貝葉斯網(wǎng)絡(luò)模型,分析各變量間的概率依賴關(guān)系,并采用10折交叉驗(yàn)證法對(duì)建模數(shù)據(jù)和2016年5月的隨訪數(shù)據(jù)進(jìn)行模型預(yù)測(cè)效果評(píng)價(jià),實(shí)現(xiàn)老年人認(rèn)知功能評(píng)價(jià)的推理及預(yù)測(cè)。結(jié)果:1、Logistic回歸模型結(jié)果顯示,年齡(OR:1.794,95%CI:1.200-2.682)、性別(OR:4.125,95%CI:2.017-8.436)、受教育程度(OR:0.633,95%CI:0.448-0.894)、抑郁程度(OR:4.458,95%CI:1.915-10.377)、高血壓(OR:2.346,95%CI:1.086-5.069)對(duì)正常老化→MCI的轉(zhuǎn)移有影響;年齡(OR:2.450,95%CI:1.212-4.953)、性別(OR:0.118,95%CI:0.031-0.442)、受教育程度(OR:0.614,95%CI:0.375-1.004)、性格(OR:0.092,95%CI:0.013-0.662)、婚姻狀況(OR:0.272,95%CI:0.086-0.862)、家庭人均收入(OR:0.456,95%CI:0.273-0.762)、身體活動(dòng)(OR:0.631,95%CI:0.407-0.980)和讀書看報(bào)(OR:0.432,95%CI:0.188-0.992)、抑郁程度(OR:97.144,95%CI:21.452-439.909)、高血壓(OR:0.304,95%CI:0.077-1.199)、腦外傷史(OR:0.188,95%CI:0.037-0.959)均對(duì)MCI→中重度認(rèn)知損害轉(zhuǎn)移有影響;性別(OR:0.328,95%CI:0.087-1.234)、婚姻狀況(OR:0.102,95%CI:0.043-0.243)、離休前職業(yè)(OR:7.799,95%CI:1.242-48.955)、飲酒(OR:0.126,95%CI:0.016-0.997)、抑郁程度(OR:3.560,95%CI:0.998-12.705)在中重度認(rèn)知損害→AD轉(zhuǎn)移中有統(tǒng)計(jì)學(xué)意義。2、用于構(gòu)建貝葉斯網(wǎng)絡(luò)結(jié)構(gòu)的節(jié)點(diǎn)包括認(rèn)知功能、抑郁程度、性別、年齡、性格、受教育程度、婚姻狀況、家庭人均收入、離休前職業(yè)、讀書看報(bào)、身體活動(dòng)、飲酒、腦外傷史、高血壓。結(jié)果顯示模型的期望損失為10.28,且貝葉斯網(wǎng)絡(luò)的預(yù)測(cè)效能優(yōu)于其他分類器效能,對(duì)認(rèn)知功能的預(yù)測(cè)準(zhǔn)確率為77.14%,靈敏度為0.869,特異度為0.770,抑郁情況的預(yù)測(cè)準(zhǔn)確率為80.07%,靈敏度為0.801,特異度為0.648。對(duì)老年人認(rèn)知功能有直接作用的是高血壓、受教育程度、離休前職業(yè)和抑郁程度,性別、家庭人均收入、婚姻狀況、讀書看報(bào)、身體活動(dòng)、性格分別通過(guò)受教育程度、離休前職業(yè)、抑郁程度、高血壓間接作用于認(rèn)知功能。結(jié)論:1、高血壓、受教育程度、離休前職業(yè)和抑郁程度與老年人認(rèn)知功能可能有直接因果關(guān)系,性別、家庭人均收入、婚姻狀況、讀書看報(bào)、身體活動(dòng)、性格可能分別通過(guò)受教育程度、離休前職業(yè)、抑郁程度、高血壓間接作用于認(rèn)知功能。老年人在平時(shí)的生活中多讀書、進(jìn)行適當(dāng)?shù)纳眢w活動(dòng)、培養(yǎng)外向的性格和適當(dāng)飲酒可能延緩認(rèn)知和記憶的退化。2、將貝葉斯網(wǎng)絡(luò)應(yīng)用于老年人認(rèn)知隊(duì)列研究,可以直觀地了解認(rèn)知功能影響因素之間的相互關(guān)系,并實(shí)現(xiàn)了因果推斷和個(gè)體的疾病風(fēng)險(xiǎn)預(yù)測(cè),表明其在醫(yī)學(xué)研究中的優(yōu)勢(shì)和在其他疾病中實(shí)踐的可行性。
[Abstract]:Objective: Based on the natural history of Alzheimer disease (AD), the Bias network model was applied to the cognitive evaluation of elderly people, the reasoning and prediction model of cognitive function evaluation for the elderly was established, and the factors affecting the cognitive function of the elderly were discussed, and the theoretical basis for the formulation of the prevention measures at different stages of AD was provided. At the same time, from the practical point of view, we excavate the advantages of Bayesian statistics in the cohort study data analysis, provide methodology for other chronic disease process research, and extend the concept of precision medicine to the risk prediction of disease. The subjects with normal aging, mild cognitive impairment (Mild Cognitive Impairment, MCI), MCI to moderate to severe cognitive impairment, moderate to severe cognitive impairment and whether or not AD transfer to construct Logistic regression model, select factors that affect cognitive degradation, and will enter all variables of the model to construct Bayesian network model and analyze the probability of each variable. Rate dependence, and using the 90% off cross validation method to evaluate the model data and the follow-up data of May 2016, to realize the reasoning and prediction of cognitive function evaluation for the elderly. Results: 1, the results of Logistic regression model show that age (OR:1.794,95%CI: 1.200-2.682), sex (OR:4.125,95%CI:2.017-8.436), education degree (OR:0.633,95%CI:0.448-0.894), the degree of depression (OR:4.458,95%CI:1.915-10.377), hypertension (OR:2.346,95%CI:1.086-5.069) had an effect on the transfer of normal aging to MCI; age (OR:2.450,95%CI:1.212-4.953), sex (OR:0.118,95%CI:0.031-0.442), educational level (OR:0.614,95%CI:0.375-1.004), character (OR:0.092,95%CI:0.013-0.662), Marital status (OR:0.272,95%CI:0.086-0.862), family per capita income (OR:0.456,95%CI:0.273-0.762), physical activity (OR:0.631,95%CI:0.407-0.980) and reading and reading (OR:0.432,95%CI:0.188-0.992), degree of depression (OR:97.144,95%CI:21.452-439.909), hypertension (OR:0.304,95%CI:0.077-1.199), and history of brain trauma (OR:0.188,95%CI:0.037-0.959) The influence of MCI to moderate severe cognitive impairment; sex (OR:0.328,95%CI:0.087-1.234), marital status (OR:0.102,95%CI:0.043-0.243), pre retirement occupation (OR:7.799,95%CI:1.242-48.955), drinking (OR:0.126,95%CI:0.016-0.997), and degree of depression (OR:3.560,95% CI:0.998-12.705) were statistically significant in moderate to severe cognitive impairment to AD metastasis. .2, the nodes used to build Bayesian network structure include cognitive function, degree of depression, sex, age, personality, education, marital status, family per capita income, pre retirement occupation, reading and reading, physical activity, drinking, brain trauma history, hypertension. The expected loss of the model is 10.28, and the predictive effectiveness of the Bayesian network is better than that of the Bayesian network. The accuracy of the cognitive function was 77.14%, the sensitivity was 77.14%, the sensitivity was 0.869, the specificity was 0.770, the accuracy of the depression was 80.07%, the sensitivity was 0.801. The specificity was the hypertension, the degree of education, the degree of occupation and depression before the rest, the sex, and the family per person. Income, marital status, reading and reading, physical activity, and character respectively through the degree of education, pre occupation, depression, and hypertension indirect effect on cognitive function. Conclusion: 1, hypertension, education, the degree of occupation and depression before retirement and the cognitive work of the elderly may have direct causal relationship, sex, family per capita income, marriage The status of reading, reading, reading, physical activity and character may be indirectly influenced by the degree of education, the occupation before the rest, the degree of depression, and the hypertension. The elderly in the normal life are reading more, carrying out proper physical activity, developing the extroverted character and drinking wine, which may delay the degenerative.2 of cognition and memory, and the Bias network Applied to the cognitive cohort study of the elderly, we can intuitively understand the relationship between the factors affecting the cognitive function, and realize the causal inference and the individual's disease risk prediction, indicating its advantages in medical research and the feasibility of practice in other diseases.
【學(xué)位授予單位】：山西醫(yī)科大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R749.16;R181.3
本文編號(hào)：2125773
[Abstract]:Objective: Based on the natural history of Alzheimer disease (AD), the Bias network model was applied to the cognitive evaluation of elderly people, the reasoning and prediction model of cognitive function evaluation for the elderly was established, and the factors affecting the cognitive function of the elderly were discussed, and the theoretical basis for the formulation of the prevention measures at different stages of AD was provided. At the same time, from the practical point of view, we excavate the advantages of Bayesian statistics in the cohort study data analysis, provide methodology for other chronic disease process research, and extend the concept of precision medicine to the risk prediction of disease. The subjects with normal aging, mild cognitive impairment (Mild Cognitive Impairment, MCI), MCI to moderate to severe cognitive impairment, moderate to severe cognitive impairment and whether or not AD transfer to construct Logistic regression model, select factors that affect cognitive degradation, and will enter all variables of the model to construct Bayesian network model and analyze the probability of each variable. Rate dependence, and using the 90% off cross validation method to evaluate the model data and the follow-up data of May 2016, to realize the reasoning and prediction of cognitive function evaluation for the elderly. Results: 1, the results of Logistic regression model show that age (OR:1.794,95%CI: 1.200-2.682), sex (OR:4.125,95%CI:2.017-8.436), education degree (OR:0.633,95%CI:0.448-0.894), the degree of depression (OR:4.458,95%CI:1.915-10.377), hypertension (OR:2.346,95%CI:1.086-5.069) had an effect on the transfer of normal aging to MCI; age (OR:2.450,95%CI:1.212-4.953), sex (OR:0.118,95%CI:0.031-0.442), educational level (OR:0.614,95%CI:0.375-1.004), character (OR:0.092,95%CI:0.013-0.662), Marital status (OR:0.272,95%CI:0.086-0.862), family per capita income (OR:0.456,95%CI:0.273-0.762), physical activity (OR:0.631,95%CI:0.407-0.980) and reading and reading (OR:0.432,95%CI:0.188-0.992), degree of depression (OR:97.144,95%CI:21.452-439.909), hypertension (OR:0.304,95%CI:0.077-1.199), and history of brain trauma (OR:0.188,95%CI:0.037-0.959) The influence of MCI to moderate severe cognitive impairment; sex (OR:0.328,95%CI:0.087-1.234), marital status (OR:0.102,95%CI:0.043-0.243), pre retirement occupation (OR:7.799,95%CI:1.242-48.955), drinking (OR:0.126,95%CI:0.016-0.997), and degree of depression (OR:3.560,95% CI:0.998-12.705) were statistically significant in moderate to severe cognitive impairment to AD metastasis. .2, the nodes used to build Bayesian network structure include cognitive function, degree of depression, sex, age, personality, education, marital status, family per capita income, pre retirement occupation, reading and reading, physical activity, drinking, brain trauma history, hypertension. The expected loss of the model is 10.28, and the predictive effectiveness of the Bayesian network is better than that of the Bayesian network. The accuracy of the cognitive function was 77.14%, the sensitivity was 77.14%, the sensitivity was 0.869, the specificity was 0.770, the accuracy of the depression was 80.07%, the sensitivity was 0.801. The specificity was the hypertension, the degree of education, the degree of occupation and depression before the rest, the sex, and the family per person. Income, marital status, reading and reading, physical activity, and character respectively through the degree of education, pre occupation, depression, and hypertension indirect effect on cognitive function. Conclusion: 1, hypertension, education, the degree of occupation and depression before retirement and the cognitive work of the elderly may have direct causal relationship, sex, family per capita income, marriage The status of reading, reading, reading, physical activity and character may be indirectly influenced by the degree of education, the occupation before the rest, the degree of depression, and the hypertension. The elderly in the normal life are reading more, carrying out proper physical activity, developing the extroverted character and drinking wine, which may delay the degenerative.2 of cognition and memory, and the Bias network Applied to the cognitive cohort study of the elderly, we can intuitively understand the relationship between the factors affecting the cognitive function, and realize the causal inference and the individual's disease risk prediction, indicating its advantages in medical research and the feasibility of practice in other diseases.
【學(xué)位授予單位】：山西醫(yī)科大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R749.16;R181.3
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