本文選題：CBR技術 + 中醫(yī)診療系統(tǒng)�。� 參考：《中國科學院大學(中國科學院工程管理與信息技術學院)》2017年碩士論文

【摘要】：論文立足于“基于古代文獻挖掘的新發(fā)突發(fā)傳染病中醫(yī)診療輔助方法”這個項目,以診斷和治療方法的確定為切入點,借助古疫病文獻開展的對于新發(fā)突發(fā)發(fā)熱中醫(yī)診療輔助方法的研究。目的是為了把人工智能(Artificial Intelligence,AI)技術中的基于案例推理(Case-based reasoning,CBR)技術和中醫(yī)學知識進行相結合,設計和開發(fā)出靈活、有效、實用且操作性強的“基于CBR技術的中醫(yī)診療系統(tǒng)”為新發(fā)突發(fā)傳染病中醫(yī)診療方案的制定提供決策支持。本文完成的主要工作如下:(1)研究了案例推理技術的理論知識。比較深入地探究了CBR技術的基本機理、進行推理的生命周期、CBR技術的應用和成長等。然后對CBR技術進行推理的整個過程中所涉及到的幾個步驟進行了詳細介紹,包括對案例的表示、案例檢索(Retrieve)、案例重用(Reuse)、案例修正(Revise)與案例保存(Retain)。(2)在Huang、Ahmad等人的研究基礎上,提出了計算混合類型數(shù)據(jù)之間相似性的度量方法。案例檢索是整個推理過程中的核心,案例檢索的效率會對用戶的體驗產生直接的影響。為了驗證所提方法的有效性,選用了醫(yī)療領域關于乳腺癌和糖尿病診斷以及銀行領域關于用戶信用判斷的數(shù)據(jù)集進行了相應的實驗。并對實驗結果進行了對比和分析,證實了提出的相似度測量方法的有效與可行。(3)把所提的混合相似度測量方法應用于基于CBR技術的中醫(yī)診療系統(tǒng)中。在收集到的中醫(yī)學治療新發(fā)突發(fā)傳染病的數(shù)據(jù)基礎上,采用CBR技術,對要開發(fā)的中醫(yī)診療系統(tǒng)的各個模塊進行了設計,選用java語言完成了整個系統(tǒng)的開發(fā)。并對系統(tǒng)中的主要功能模塊進行了展示�；贑BR技術的中醫(yī)診療系統(tǒng)的研發(fā),不單單可以使后人利用歷代中醫(yī)學家積累的治病經歷和智慧來輔助中醫(yī)治療師進行決策,從而提高臨床診療效果,減少誤診事件的發(fā)生;而且還可以對那些古人積累的中醫(yī)學知識進行很好的保存,便于一些教育機構用于教學使用,實現(xiàn)中醫(yī)學知識的共享和傳承,更好的服務于人類。
[Abstract]:This paper is based on the project of "the assistant method of TCM diagnosis and treatment of emerging infectious diseases based on the ancient literature mining", which starts with the determination of diagnosis and treatment methods. The study on the assistant method of diagnosis and treatment of new sudden fever in TCM with the aid of ancient blight literature. The purpose of this paper is to combine Case-based reasoning (CBR) technology with traditional Chinese medicine (TCM) knowledge in Artificial Intelligence AI (AI) technology, and to design and develop flexible and effective Case-based reasoning (CBR) technology. The practical and operable Chinese medicine diagnosis and treatment system based on CBR technology provides decision support for the formulation of new infectious disease diagnosis and treatment scheme. The main work of this paper is as follows: (1) the theoretical knowledge of Case-Based reasoning (CBR) is studied. The basic mechanism of CBR technology and the application and growth of CBR technology are discussed. Then, several steps involved in the whole process of reasoning are introduced in detail, including case representation, case retrieval, case reuse, case revision and case). (2. A method to measure the similarity between mixed type data is proposed. Case retrieval is the core of the whole reasoning process, and the efficiency of case retrieval will have a direct impact on the user's experience. In order to verify the effectiveness of the proposed method, the data sets of breast cancer and diabetes diagnosis in the medical field and user credit judgment in the banking field were selected for corresponding experiments. The comparison and analysis of the experimental results show that the proposed similarity measurement method is effective and feasible. (3) the proposed hybrid similarity measurement method is applied to the traditional Chinese medicine diagnosis and treatment system based on CBR technology. On the basis of the collected data on the treatment of emerging infectious diseases in traditional Chinese medicine (TCM), the various modules of the TCM diagnosis and treatment system to be developed were designed by using CBR technology, and the whole system was developed with java language. The main function modules of the system are also shown. The research and development of TCM diagnosis and treatment system based on CBR technology can not only make future generations make use of the experience and wisdom accumulated by TCM scientists to assist TCM therapists to make decisions, so as to improve the effect of clinical diagnosis and treatment and reduce the occurrence of misdiagnosis. It can also preserve the knowledge of traditional Chinese medicine accumulated by the ancients, which is convenient for some educational institutions to use in teaching, to realize the sharing and transmission of traditional Chinese medicine knowledge, and to better serve human beings.
【學位授予單位】：中國科學院大學(中國科學院工程管理與信息技術學院)
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R24;TP18

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