本文選題：貝葉斯網(wǎng)絡(luò) + 肝性腦病��； 參考：《山西醫(yī)科大學(xué)》2017年碩士論文

【摘要】：目的:建立肝硬化并發(fā)肝性腦病相關(guān)因素的貝葉斯網(wǎng)絡(luò)模型,探索肝性腦病與這些可能因素間的關(guān)系,通過貝葉斯網(wǎng)絡(luò)推理反映一個或多個因素對肝性腦病的作用強(qiáng)度;嘗試構(gòu)建肝性腦病分類識別模型,探討貝葉斯網(wǎng)絡(luò)用于肝性腦病識別的分類效果,為臨床醫(yī)生識別肝性腦病提供合理的方法,為肝性腦病的智能識別奠定前期基礎(chǔ)。方法:收集2006年1月~2015年12月在山西醫(yī)科大學(xué)第一附屬醫(yī)院消化內(nèi)科住院治療并具有完整病歷資料的950例肝硬化患者,利用單因素及多因素logistic回歸分析篩選出肝硬化并發(fā)肝性腦病的相關(guān)因素,構(gòu)建貝葉斯網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)。然后在單因素篩選出肝性腦病相關(guān)因素的基礎(chǔ)上,采用禁忌搜索算法構(gòu)建貝葉斯網(wǎng)絡(luò)(GBN),探討GBN用于肝性腦病識別的分類效能,同時與樸素貝葉斯分類器(NB)、樹擴(kuò)展樸素貝葉斯分類器(TAN)及l(fā)ogistic概率預(yù)測模型的識別能力進(jìn)行比較。結(jié)果:1.將與肝性腦病有關(guān)系的23個因素進(jìn)行單因素及多因素logistic回歸分析,結(jié)果顯示最終進(jìn)入回歸模型的因素有7個,電解質(zhì)紊亂,感染,精神萎靡,肝腎綜合征,肝源性糖尿病及凝血酶原時間延長、總膽紅素升高均與肝性腦病有關(guān);其中,電解質(zhì)紊亂、肝腎綜合征、感染是肝硬化患者并發(fā)肝性腦病的主要危險因素,其風(fēng)險分別為6.861倍、3.467倍、3.021倍;精神萎靡、肝源性糖尿病、凝血酶原時間延長、總膽紅素升高與HE的關(guān)系相近,相對危險度在2.1~2.7范圍。2.構(gòu)建的貝葉斯網(wǎng)絡(luò)ROC曲線下的面積為0.843,網(wǎng)絡(luò)評價效果較好。各因素通過復(fù)雜的關(guān)系與肝性腦病建立聯(lián)系,其中肝腎綜合征、電解質(zhì)紊亂、感染、精神萎靡、總膽紅素和凝血酶原時間與肝性腦病有直接關(guān)系,肝源性糖尿病通過總膽紅素間接與肝性腦病發(fā)生關(guān)聯(lián)。網(wǎng)絡(luò)推理發(fā)現(xiàn),感染、電解質(zhì)紊亂和肝腎綜合征與肝性腦病的關(guān)系更密切。3.根據(jù)本研究950例肝硬化數(shù)據(jù)集的分類識別效果評價可看出,禁忌搜索算法構(gòu)建的貝葉斯網(wǎng)絡(luò)(GBN)對肝性腦病的整體識別效能高于其他模型(F-measure為0.410,G-measure為0.739);經(jīng)過1:2抽樣,縮小兩類間樣本數(shù)的不平衡現(xiàn)象后,GBN的G-measure值為0.754,僅次于TAN,而GBN的F-measure值依然高于其它模型(F-measure值為0.820)。相對于950例不平衡數(shù)據(jù)集,GBN的G-measure值增加了2.0%,F-measure值增加了1倍,說明GBN對肝性腦病的整體識別效能有所提高,尤其對肝硬化并發(fā)肝性腦病的陽性識別性能提升幅度較大,在約登指數(shù)最大的條件下,概率截斷點(diǎn)由0.10提高為0.374,該水平在識別HE時更為合理。結(jié)論:禁忌搜索算法構(gòu)建的貝葉斯網(wǎng)絡(luò)靈敏度特異度高,能反映各節(jié)點(diǎn)之間的關(guān)系,能夠揭示相關(guān)因素間的聯(lián)系及對肝性腦病的作用,貝葉斯網(wǎng)絡(luò)推理可以根據(jù)醫(yī)生掌握患者信息的先后順序,對患者并發(fā)肝性腦病進(jìn)行推理,符合臨床診療序貫過程。嘗試用禁忌搜索算法構(gòu)建的貝葉斯網(wǎng)絡(luò)(GBN)用于肝硬化并發(fā)肝性腦病的識別,其對肝性腦病陽性分類識別能力高于其它模型,對肝性腦病的篩查可能有一定的指導(dǎo)意義,但是用于臨床肝硬化并發(fā)肝性腦病分類識別效果還需要外部數(shù)據(jù)的驗證。
[Abstract]:Objective: to establish the Bayesian network model related factors of liver cirrhosis complicated with hepatic encephalopathy, to explore the relationship between hepatic encephalopathy and the possible factors, by the Bayesian network inference reflects one or more factors on the strength of hepatic encephalopathy; try to construct the hepatic encephalopathy classification recognition model of Bayesian network for classification of hepatic encephalopathy provides recognition. A reasonable method for clinicians to identify hepatic encephalopathy, will lay the foundation for intelligent recognition of hepatic encephalopathy. Methods: from January 2006 December ~2015 in 950 cases of liver cirrhosis patients in the First Affiliated Hospital of Shanxi Medical University Department of Gastroenterology Hospital treatment and with complete medical records, analysis showed that the related factors of liver cirrhosis complicated with hepatic encephalopathy using univariate and multivariate logistic regression, to construct a Bayesian network topology. Then the single factor screening of hepatic encephalopathy. Based on the factors of constructing the Bias network using tabu search algorithm (GBN), to investigate the classification performance of GBN for hepatic encephalopathy recognition, and at the same time Naive Bayesian classifier (NB), extended Naive Bayesian tree classifier (TAN) model recognition ability to compare prediction and probability of logistic. Results: 23 factors and 1. the relationship between hepatic encephalopathy logistic univariate and multivariate regression analysis, the results show the final factors into the regression model 7, electrolyte disorder, infection, listlessness, hepatorenal syndrome, hepatic diabetes and prolonged prothrombin time, total bilirubin increased associated with hepatic encephalopathy; among them, electrolyte disorder hepatorenal syndrome, infection is a major risk factor in patients with liver cirrhosis complicated with hepatic encephalopathy, the risk was 6.861 times, 3.467 times, 3.021 times; listlessness, hepatogenic diabetes, prothrombin time. Long, total bilirubin increased close relationship with HE, Bias network ROC curve of relative risk in the range of 2.1~2.7.2. under the construction area of 0.843, better network evaluation effect. Various factors through the complicated relationship with hepatic encephalopathy which establish contact, hepatorenal syndrome, electrolyte disorder, infection, listlessness, total bilirubin direct and the relationship between prothrombin time and hepatic encephalopathy, hepatogenic diabetes through total bilirubin encephalopathy and indirect association. Network inference, infection, electrolyte disorder and hepatorenal syndrome, hepatic encephalopathy and more closely.3. according to the classification results in this study, 950 cases of liver cirrhosis of the evaluation data set can be seen, the tabu search algorithm Bias network the construction of the overall recognition performance (GBN) of hepatic encephalopathy is higher than other models (F-measure = 0.410, G-measure = 0.739); after 1:2 sampling, narrow between two classes Imbalance of sample number, GBN G-measure value is 0.754, second only to TAN, GBN and F-measure value is still higher than that of other models (F-measure = 0.820). Compared with 950 cases of imbalanced data sets, GBN G-measure value increased by 2%, F-measure increased by 1 times, indicating that the overall recognition performance of hepatic GBN encephalopathy has increased, especially the positive recognition performance of liver cirrhosis complicated with hepatic encephalopathy improved greatly, the Youden index under the condition of the maximum probability, the cut-off point was improved from 0.10 to 0.374, the level of recognition in HE is more reasonable. Conclusion: the sensitivity of the Bayesian network can avoid constructing search algorithm for high specificity, can reflect the relationship between between the various nodes, can reveal the relationship between related factors of hepatic encephalopathy and the role of Bayesian network inference can be based on the order of the doctor grasp of the information of the patients, for patients with hepatic encephalopathy In accordance with the clinical diagnosis and treatment of sequential reasoning process. Try the search algorithm for Bayesian network constructed by Tabu (GBN) for identification of liver cirrhosis complicated with hepatic encephalopathy, the hepatic encephalopathy positive recognition ability than other models, screening for hepatic encephalopathy may have a certain guiding significance, but for clinical liver cirrhosis complicated with hepatic encephalopathy classification effect need to verify the external data.

【學(xué)位授予單位】：山西醫(yī)科大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R575.2;R747.9

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