本文選題：醫(yī)學(xué)數(shù)據(jù)挖掘　切入點：BP神經(jīng)網(wǎng)絡(luò)算法　出處：《中山大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著生物醫(yī)學(xué)工程的迅猛發(fā)展，測量儀器技術(shù)的提高，大量醫(yī)療數(shù)據(jù)被精確地記錄下來，從而導(dǎo)致醫(yī)療數(shù)據(jù)資料爆炸性增長，因此數(shù)據(jù)挖掘技術(shù)被廣泛應(yīng)用于醫(yī)學(xué)領(lǐng)域以發(fā)現(xiàn)海量數(shù)據(jù)中潛在的知識。在本院與中山大學(xué)醫(yī)學(xué)院合作的交叉學(xué)科項目中，醫(yī)學(xué)院提出了采用新興的生物反饋療法對高血壓前期進行干預(yù)作用，在治療中醫(yī)生通過引導(dǎo)患者調(diào)節(jié)心率變異性（HRV）以達到調(diào)節(jié)血壓的功效。這一治療方法已經(jīng)積累了（并且將持續(xù)積累）大量治療方案、生物體征數(shù)據(jù)等，本文的研究動機就是將數(shù)據(jù)挖掘技術(shù)應(yīng)用在治療數(shù)據(jù)上，通過這些數(shù)據(jù)預(yù)測患者治療的有效性以及治療后的HRV值，為醫(yī)生在治療過程中的決策提供指導(dǎo)依據(jù)。 本文在學(xué)習(xí)了生物反饋領(lǐng)域知識的基礎(chǔ)上，提取出兩個挖掘任務(wù)并建立了分類和預(yù)測模型，主要流程如下：1）根據(jù)領(lǐng)域知識進行特征選擇，進行數(shù)據(jù)預(yù)處理后建立治療有效性的分類預(yù)測模型以此提高醫(yī)生治療的針對性，并且對比了C4.5算法和隨機森林算法，實驗顯示隨機森林算法模型的準確率高于C4.5；2）以患者治療前，治療中的各體征值建立HRV值的回歸預(yù)測模型來幫助醫(yī)生更準確地找到HRV目標(biāo)值，并且對比了BP神經(jīng)網(wǎng)絡(luò)算法和多元回歸算法，實驗顯示BP神經(jīng)網(wǎng)絡(luò)算法誤差較��；3）最后設(shè)計與實現(xiàn)了生物反饋治療輔助系統(tǒng)，，以更好的人機交互和操作流程可視化方式將效果較好的隨機森林算法和BP神經(jīng)網(wǎng)絡(luò)算法模型應(yīng)用在該系統(tǒng)中。 本文依照實證研究的方法，在26個患者治療過程實例中收集醫(yī)生預(yù)測的結(jié)果，并對比本文系統(tǒng)的預(yù)測結(jié)果。實驗證明本文分類和預(yù)測模型的準確率都高于醫(yī)生的經(jīng)驗預(yù)測結(jié)果，因此預(yù)測結(jié)果在醫(yī)生設(shè)計生物反饋治療方案過程中起到了指導(dǎo)作用，有一定的臨床意義。同時本文實現(xiàn)的治療反饋輔助系統(tǒng)將數(shù)據(jù)挖掘知識應(yīng)用在實際中，集成了治療過程中情緒問卷調(diào)查、血壓對比等功能，優(yōu)化了醫(yī)生的治療流程，提高了研究療效的效率。
[Abstract]:With the rapid development of Biomedical Engineering, instrumentation technology, a large number of medical data are accurately recorded, resulting in the explosive growth of medical data, so data mining technology has been widely applied in the field of medicine in order to find out the potential massive data knowledge. In this interdisciplinary project in cooperation with the Institute of Zhongshan University School of Medicine School of medicine, put forward the emerging bio feedback therapy intervention effect on hypertension in the early treatment of Chinese students by guiding the patients to regulate the heart rate variability (HRV) in order to regulate the blood pressure effect. This treatment method has been accumulated (and will continue to accumulate a large number of) treatment, biometric data, study motivation this paper is the application of data mining technology in the treatment of the data, through these data to predict patient effectiveness of treatment and after treatment HRV value for doctors Provide guidance for decision making in the course of treatment.
In this paper, learning the basic knowledge in the field of biological feedback, extract two mining tasks and established a classification and prediction model, the main process is as follows: 1) feature selection based on domain knowledge, data preprocessing is carried out after the establishment of a prediction model for the effectiveness of this to improve the relevance of medical treatment, and compared the C4.5 algorithm and random forest algorithm, experiments show that the accuracy of the random forest algorithm model is higher than that of C4.5; 2) in patients before treatment, the symptoms in the treatment of value regression HRV value prediction model to help doctors more accurately find the target value of HRV, and compared with the BP neural network algorithm and regression algorithm, experiments show BP neural network algorithm has smaller error; 3) the design and Realization of the biofeedback assisted system, in order to better human-computer interaction and operation flow visualization will better effect with The computer forest algorithm and the BP neural network algorithm model are applied to the system.
According to the method of empirical research, in 26 of patients in the process instance to collect the doctor predicted results, and compared the system prediction results. The experiment results show that the prediction results of the doctor is higher than the accuracy of this classification and prediction model of the experience, so the prediction result has played a guiding role in the design of the doctor biofeedback treatment process, has a certain clinical significance. The treatment and the auxiliary feedback system will be the knowledge of data mining applications in integrated treatment of emotion questionnaire survey, comparison of blood pressure and other functions, optimizing the medical treatment process, improve the efficiency of treatment.

【學(xué)位授予單位】：中山大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：R318.6;TP311.13

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