本文選題：本體 + 自動診斷　； 參考：《電子科技大學(xué)》2017年碩士論文

【摘要】：隨著我國人口數(shù)量地增多,人口老齡化趨勢越來越嚴峻,同時人們生活水平不斷地提高,人們更關(guān)注自己的生命健康狀況。目前,醫(yī)生依舊采取問診和醫(yī)學(xué)檢查相結(jié)合的方式對病人進行疾病診斷,但是,我國醫(yī)療水平發(fā)展在不同地區(qū)、不同醫(yī)院之間存在不均衡的問題,醫(yī)生的能力也存在差異,許多病人為了確診所患的疾病將花費高昂的費用。如何根據(jù)癥狀來自動化地診斷病人所患疾病是本文研究的重點。在過去的二十多年,本體已經(jīng)廣泛應(yīng)用到知識工程、人工智能、信息推薦、自然語言處理、生物信息學(xué)、農(nóng)業(yè)領(lǐng)域等工程�；诒倔w的應(yīng)用越來越多,然而,許多情況下本體是手工構(gòu)建,存在工作量大、效率低、關(guān)系表達錯誤等缺點。本文在形式概念和本體理論的基礎(chǔ)上,把本體作為知識表達和共享的載體,將癥狀、疾病知識組織起來,建立疾病與癥狀的本體知識庫,以便實現(xiàn)醫(yī)療的自動診斷。綜上所述,本文研究的主要內(nèi)容如下:1.在構(gòu)建疾病與癥狀的本體過程中,改進了屬性偏序結(jié)構(gòu)圖算法,并利用該算法自動化地構(gòu)建疾病本體知識庫。針對疾病數(shù)據(jù)集,提出了癥狀信息量的概念來刻畫具有某些癥狀的疾病數(shù)量,并選取具有最小信息量的公共癥狀集合,構(gòu)建屬性偏序結(jié)構(gòu)圖。實驗結(jié)果表明,利用最小信息量的公共癥狀集合構(gòu)建偏序結(jié)構(gòu)圖,降低了多個癥狀之間的冗余性。2.針對疾病數(shù)據(jù)集,分析了構(gòu)建的疾病與癥狀的本體結(jié)構(gòu),提出了一套用于病人疾病診斷的醫(yī)療診斷模型。在醫(yī)療診斷模型中,利用癥狀之間的相似度與癥狀的權(quán)重進行加權(quán)平均,計算出病人疾病與該疾病之間的相似度,并利用該相似度來衡量病人患有該疾病的概率。實驗結(jié)果表明,該算法在急性膀胱炎和急性腎炎的疾病數(shù)據(jù)中進行醫(yī)療診斷的準(zhǔn)確率都在80%以上。3.設(shè)計并實現(xiàn)了醫(yī)療自動診斷系統(tǒng)。該系統(tǒng)使用MVC設(shè)計模式,使系統(tǒng)界面顯示和疾病診斷過程相互獨立。在進行醫(yī)療自動診斷時候,醫(yī)生采用問診的形式,確定病人具有的癥狀,系統(tǒng)自動根據(jù)病人的癥狀進行醫(yī)療診斷并輸出診斷結(jié)果。經(jīng)測試,該系統(tǒng)能夠便捷地對病人進行醫(yī)療診斷。
[Abstract]:With the increase of population in our country, the aging trend of population is becoming more and more serious, and people's living standard is improving constantly, so people pay more attention to their own life and health. At present, doctors still use the combination of examination and medical examination to diagnose patients' diseases. However, the medical level of our country is developing in different regions, there are uneven problems among different hospitals, and the ability of doctors is also different. Many patients will be expensive to diagnose the disease. How to diagnose the patient's disease automatically according to the symptoms is the focus of this paper. In the past twenty years, ontology has been widely used in knowledge engineering, artificial intelligence, information recommendation, natural language processing, bioinformatics, agriculture and so on. There are more and more applications based on ontology, however, in many cases ontology is constructed by hand, which has the disadvantages of heavy workload, low efficiency, wrong expression of relationship and so on. Based on the formal concept and ontology theory, this paper takes ontology as the carrier of knowledge expression and sharing, organizes the knowledge of symptoms and diseases, and establishes the ontology knowledge base of diseases and symptoms in order to realize the automatic diagnosis of medical treatment. To sum up, the main contents of this study are as follows: 1. In the process of constructing the ontology of disease and symptom, the algorithm of attribute partial order structure graph is improved, and the knowledge base of disease ontology is constructed automatically by using this algorithm. According to the disease data set, the concept of symptom information quantity is proposed to describe the number of diseases with some symptoms, and the common symptom set with minimum information is selected to construct the attribute partial order structure. The experimental results show that the least amount of common symptom set is used to construct the partial order structure chart, which reduces the redundancy of multiple symptoms. 2. According to the disease data set, this paper analyzes the ontology structure of the disease and symptom, and puts forward a set of medical diagnosis model for the patient's disease diagnosis. In the medical diagnosis model, the similarity between the symptoms and the weight of the symptoms is weighted average, and the similarity between the disease and the disease is calculated, and the probability of the patient suffering from the disease is measured by the similarity. The experimental results show that the accuracy of the proposed algorithm in the diagnosis of acute cystitis and acute glomerulonephritis is over 80%. A medical automatic diagnosis system is designed and implemented. The system uses MVC design pattern to make the system interface display and disease diagnosis process independent of each other. In automatic medical diagnosis, doctors use the form of inquiry to determine the symptoms of patients, the system automatically according to the symptoms of patients for medical diagnosis and output diagnosis results. The test results show that the system can be used to diagnose patients conveniently.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R318;TP391.1

【參考文獻】

相關(guān)期刊論文 前10條

1 李耀華;楊海燕;;論網(wǎng)絡(luò)爬蟲搜索策略[J];山西廣播電視大學(xué)學(xué)報;2013年02期

2 劉宏哲;須德;;基于本體的語義相似度和相關(guān)度計算研究綜述[J];計算機科學(xué);2012年02期

3 張亮;;基于HTMLParser和HttpClient的網(wǎng)絡(luò)爬蟲原理與實現(xiàn)[J];電腦編程技巧與維護;2011年20期

4 龍樹全;趙正文;唐華;;中文分詞算法概述[J];電腦知識與技術(shù);2009年10期

5 戴軍;陶紅兵;余瑤;陳璞;苗衛(wèi)軍;郭石林;;臨床路徑醫(yī)療質(zhì)量管理模式對醫(yī)療機構(gòu)監(jiān)管的意義與展望[J];中國醫(yī)院管理;2008年07期

6 韓婕;向陽;;本體構(gòu)建研究綜述[J];計算機應(yīng)用與軟件;2007年09期

7 董振東;董強;郝長伶;;知網(wǎng)的理論發(fā)現(xiàn)[J];中文信息學(xué)報;2007年04期

8 哈金才;;數(shù)據(jù)挖掘算法的評價標(biāo)準(zhǔn)與方法[J];微電子學(xué)與計算機;2006年12期

9 邢春艷;;基于XML/RDF和Ontology的語義網(wǎng)之構(gòu)建[J];沈陽教育學(xué)院學(xué)報;2006年03期

10 張曉梅;李丹亞;胡鐵軍;;一體化醫(yī)學(xué)語言系統(tǒng)與本體論研究[J];醫(yī)學(xué)信息學(xué)雜志;2006年02期

相關(guān)博士學(xué)位論文 前1條

1 欒景民;基于屬性偏序結(jié)構(gòu)數(shù)學(xué)原理的中醫(yī)數(shù)量化辨證診斷輔助系統(tǒng)研究[D];燕山大學(xué);2014年

相關(guān)碩士學(xué)位論文 前3條

1 付維娟;基于臨床診療決策的知識推理引擎研究與實現(xiàn)[D];西安電子科技大學(xué);2014年

2 李清;一體化醫(yī)學(xué)語言系統(tǒng)的語義相似度及推理研究[D];哈爾濱工業(yè)大學(xué);2012年

3 趙汝楠;XML到RDF(S)轉(zhuǎn)換方法的研究[D];東北大學(xué);2012年

，

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