本文選題：糖尿病　切入點(diǎn)：健康數(shù)據(jù)分析　出處：《哈爾濱工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著大眾健康意識(shí)的日益提高,普通公民對(duì)糖尿病健康服務(wù)也提出了新的需求。人們希望能盡早預(yù)知糖尿病風(fēng)險(xiǎn),足不出戶地掌握自身病情狀態(tài)。為滿足這一需求,智能健康產(chǎn)業(yè)應(yīng)運(yùn)而生。當(dāng)前,互聯(lián)網(wǎng)時(shí)代積累了大量的糖尿病健康數(shù)據(jù),包括診斷記錄、病歷信息、電子檔案等;各種各樣的健康監(jiān)測(cè)設(shè)備層出不窮,實(shí)現(xiàn)了個(gè)人日常健康信息的隨時(shí)收集和存儲(chǔ)。如何充分利用上述數(shù)據(jù),為人們提供便利的糖尿病自主評(píng)估服務(wù),進(jìn)而實(shí)現(xiàn)降低我國糖尿病發(fā)病率的目的,成為當(dāng)前亟待解決的問題。應(yīng)用計(jì)算機(jī)技術(shù)對(duì)糖尿病健康數(shù)據(jù)進(jìn)行分析,是實(shí)施健康預(yù)測(cè)和輔助診斷的有效解決方案。本文結(jié)合現(xiàn)有的糖尿病醫(yī)學(xué)評(píng)估工具的不足,對(duì)糖尿病健康數(shù)據(jù)分析方法展開研究,并進(jìn)行對(duì)應(yīng)的系統(tǒng)開發(fā),以提供糖尿病相關(guān)的健康咨詢服務(wù)。本研究主要涉及以下幾個(gè)方面:首先,為了解決糖尿病的風(fēng)險(xiǎn)識(shí)別和預(yù)測(cè)問題,進(jìn)行糖尿病風(fēng)險(xiǎn)計(jì)算方法的研究。在抽象化糖尿病輸入信息并量化風(fēng)險(xiǎn)參數(shù)的基礎(chǔ)上,挖掘?qū)嶋H健康數(shù)據(jù)中的規(guī)律,建立一個(gè)基于支持向量機(jī)(SVM)的糖尿病風(fēng)險(xiǎn)計(jì)算模型。該模型處理用戶的輸入記錄,計(jì)算出用戶患糖尿病的風(fēng)險(xiǎn),以實(shí)現(xiàn)糖尿病早識(shí)別、早預(yù)防和早治療的目標(biāo)。其次,為了合理利用糖尿病的遺傳特征,確保糖尿病風(fēng)險(xiǎn)預(yù)測(cè)的準(zhǔn)確性,建立一種糖尿病遺傳因素提取機(jī)制。該機(jī)制結(jié)合相關(guān)的醫(yī)學(xué)知識(shí),通過追溯糖尿病家族史繪制遺傳關(guān)系圖。并提出相應(yīng)的遺傳特征提取算法,用于充分提取用戶的先天性疾病信息。將該機(jī)制用于糖尿病風(fēng)險(xiǎn)計(jì)算模型,將有效地提高模型的綜合性能。再次,為了實(shí)現(xiàn)對(duì)糖尿病動(dòng)態(tài)疾病信息的預(yù)測(cè),進(jìn)行動(dòng)態(tài)血糖預(yù)測(cè)方法的研究。提出動(dòng)態(tài)血糖預(yù)測(cè)模型,將動(dòng)態(tài)血糖數(shù)據(jù)進(jìn)行提取和表達(dá),并基于深度信念網(wǎng)(DBN)探索現(xiàn)有的血糖時(shí)間序列,以預(yù)知未來一段時(shí)間內(nèi)的血糖。該模型可以幫助用戶動(dòng)態(tài)掌握和預(yù)測(cè)血糖水平。最后,在上述工作的基礎(chǔ)上,設(shè)計(jì)并實(shí)現(xiàn)一個(gè)糖尿病輔助評(píng)估系統(tǒng),從架構(gòu)、數(shù)據(jù)庫、業(yè)務(wù)功能、用戶接口等方面完成算法應(yīng)用化。
[Abstract]:With the increasing awareness of public health, ordinary citizens have put forward a new demand for diabetes health services. People hope to be able to predict the risk of diabetes as soon as possible, and master their state of illness without leaving home. The intelligent health industry came into being. At present, the Internet era has accumulated a large amount of diabetes health data, including diagnostic records, medical records, electronic files, etc. How to make full use of the above data, and how to provide people with convenient diabetes assessment services, and then achieve the goal of reducing the incidence of diabetes in China. The application of computer technology to the analysis of diabetes health data is an effective solution for the implementation of health prediction and auxiliary diagnosis. Research on diabetes health data analysis method, and corresponding system development to provide diabetes related health counseling services. This study mainly involves the following aspects: first, In order to solve the problem of risk identification and prediction of diabetes mellitus, the risk calculation method of diabetes mellitus is studied. On the basis of abstracting the input information of diabetes mellitus and quantifying the risk parameters, the rules of actual health data are excavated. A diabetes risk calculation model based on support vector machine (SVM) is established. The model processes the user's input record and calculates the user's risk of developing diabetes, so as to achieve the goal of early recognition, early prevention and early treatment of diabetes. In order to make rational use of the genetic characteristics of diabetes mellitus and ensure the accuracy of diabetes risk prediction, a mechanism for extracting genetic factors from diabetes mellitus was established. By tracing back the family history of diabetes, the genetic relationship map was drawn, and the corresponding genetic feature extraction algorithm was put forward, which was used to fully extract the information of the user's congenital disease, and the mechanism was applied to the diabetes risk calculation model. It will improve the comprehensive performance of the model effectively. Thirdly, in order to predict the dynamic disease information of diabetes mellitus, the dynamic blood glucose prediction method is studied. A dynamic blood glucose prediction model is proposed to extract and express the dynamic blood sugar data. And based on DBN (depth belief Network), we explore the existing blood glucose time series to predict the blood sugar in the future. This model can help users to grasp and predict the blood sugar level dynamically. Finally, based on the above work, A diabetes aided evaluation system is designed and implemented. The algorithm is applied from the aspects of architecture, database, business function, user interface and so on.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R587.1;TP311.13

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