本文選題：中醫(yī)醫(yī)案 + 主題模型　； 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：中醫(yī)是中國的國粹之一,已經(jīng)經(jīng)歷了幾千年的發(fā)展。中醫(yī)醫(yī)案作為中醫(yī)傳承的重要載體,體現(xiàn)了中醫(yī)理、法、方、藥的綜合運(yùn)用,蘊(yùn)含了歷代名醫(yī)豐富的臨床診療經(jīng)驗(yàn),對于中醫(yī)的學(xué)習(xí)、研究和發(fā)展具有"宣明往范,昭示來學(xué)"的作用。然而,醫(yī)案的文體多樣、文白混雜、標(biāo)準(zhǔn)化欠佳等特性,對醫(yī)案的分類、組織和分析挖掘帶來了極大的挑戰(zhàn)。同時(shí),中醫(yī)藥領(lǐng)域也缺乏專業(yè)的醫(yī)案知識服務(wù)系統(tǒng)。本文以"中國工程科技知識中心"項(xiàng)目的醫(yī)案系統(tǒng)建設(shè)為研究背景,以分析、挖掘、展示醫(yī)案中隱含的知識為目標(biāo),主要關(guān)注醫(yī)案系統(tǒng)關(guān)鍵技術(shù)應(yīng)用研究以及醫(yī)案系統(tǒng)的設(shè)計(jì)與實(shí)現(xiàn),主要工作有:1)針對醫(yī)案類別欠缺的問題,提出了一種基于主題模型的醫(yī)案分類方法,將醫(yī)案中的中藥、方劑、疾病、癥狀、證候和治法詞匯與非概念詞匯區(qū)別開。該模型能夠發(fā)現(xiàn)六類詞在每一主題下的關(guān)聯(lián)關(guān)系,進(jìn)而學(xué)得更具區(qū)分度的文本特征表示,提升了醫(yī)案分類的準(zhǔn)確率。2)為確保每篇醫(yī)案的獨(dú)特性,使用結(jié)合規(guī)則的Simhash算法對醫(yī)案文本進(jìn)行去重,同時(shí)保證了醫(yī)案集的豐富性與多樣性。3)在對處方進(jìn)行分析,發(fā)現(xiàn)與之相近的經(jīng)典方劑過程中,為了更好地解析處方,提出基于卷積神經(jīng)網(wǎng)絡(luò)的處方識別方法。該方法以句子為分割粒度,從醫(yī)案中自動(dòng)提取處方,進(jìn)而體現(xiàn)醫(yī)家在治病過程中遣方用藥的規(guī)律。4)為提高服務(wù)數(shù)據(jù)的精準(zhǔn)度,提出了一套眾包方案,通過用戶提交意見,專家審核的方法對系統(tǒng)中的有誤數(shù)據(jù)進(jìn)行修正。通過少數(shù)服從多數(shù)算法和DawidSkene算法對用戶意見進(jìn)行質(zhì)量控制。5)基于以上研究,設(shè)計(jì)并實(shí)現(xiàn)了醫(yī)案系統(tǒng),提供醫(yī)案搜索、分類瀏覽、醫(yī)書閱讀、處方分析、醫(yī)案分析、錯(cuò)誤修正等服務(wù),并已上線運(yùn)行。
[Abstract]:Chinese medicine is one of the quintessence of China, has experienced thousands of years of development. As an important carrier of the inheritance of TCM, TCM medical records embody the comprehensive application of TCM principles, methods, prescriptions and medicines, and contain rich clinical experience of famous doctors in the past dynasties, which has a "clear and clear model" for the study, research and development of TCM. The role of learning. However, the characteristics of medical records, such as diverse style, mixed text and poor standardization, bring great challenges to the classification, organization and analysis of medical records. At the same time, the Chinese medicine field also lacks the specialized medical record knowledge service system. This paper takes the construction of medical record system of "China Engineering Science and Technology knowledge Center" as the research background, and aims at analyzing, excavating and displaying the hidden knowledge in medical records. Focusing on the application of the key technology of medical record system and the design and implementation of medical record system, the main work includes: (1) aiming at the problem of lack of medical record category, a method of classifying medical case based on subject model is put forward, in which traditional Chinese medicine and prescription in medical record are classified. Diseases, symptoms, syndromes and therapeutic vocabulary are distinguished from non-conceptual vocabulary. The model can discover the relationship between the six categories of words under each topic, and then learn a more differentiated text feature representation, which improves the accuracy of medical case classification. 2) in order to ensure the uniqueness of each medical case, The Simhash algorithm combined with rules is used to remove the text of medical records, and at the same time, the richness and diversity of medical records are ensured. 3) in the course of analyzing the prescriptions, we find out that in the process of finding similar classical prescriptions, in order to better analyze the prescriptions, A method of prescription recognition based on convolution neural network is proposed. In order to improve the accuracy of service data, the method takes sentence as segmentation granularity, automatically extracts prescriptions from medical cases, and then reflects the rule of medicine used by doctors in the course of treatment. In order to improve the accuracy of service data, a crowdsourcing scheme is put forward, and the opinions are submitted by users. The method of expert audit corrects the incorrect data in the system. Based on the above research, a medical record system is designed and implemented, which provides medical case search, classification browsing, medical book reading, prescription analysis, medical case analysis, etc. Error correction and other services, and has been online.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP311.52;TP391.1

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