本文關(guān)鍵詞：數(shù)據(jù)挖掘算法研究及其在處方分析系統(tǒng)的應(yīng)用　出處：《南京理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 數(shù)據(jù)挖掘 關(guān)聯(lián)規(guī)則 粗糙集 處方分析系統(tǒng)

【摘要】：中國醫(yī)學(xué)是中華名族的瑰寶,中藥作為中醫(yī)的主要依托,在中醫(yī)理論中發(fā)揮了巨大作用。千百年來中國人民的健康問題基本上是靠著看中醫(yī)、吃中藥解決的。隨著中醫(yī)院信息化的建設(shè),醫(yī)院數(shù)據(jù)庫中存儲了大量的中醫(yī)處方。因此,利用興起的數(shù)據(jù)挖掘技術(shù),將傳統(tǒng)的中醫(yī)處方和現(xiàn)代的信息處理技術(shù)相結(jié)合,可以在沒有先驗知識的情況下,對藥材之間的關(guān)系和處方中蘊含的規(guī)律進行挖掘,該研究有良好的應(yīng)用前景。本文介紹了基于關(guān)聯(lián)規(guī)則的經(jīng)典Apriori算法,分析利用并行思想改進的計數(shù)分布算法(CD算法)。針對傳統(tǒng)算法的不足,提出基于矩陣的并行關(guān)聯(lián)規(guī)則挖掘算法(PM算法),解決傳統(tǒng)串行算法速度的瓶頸。和CD算法相比,PM算法可以更快的生成頻繁項集。本文分析了傳統(tǒng)的粗糙集屬性約簡算法,通過對其建模分析,提出了基于模擬退火的屬性約簡算法,并將算法運用到條件屬性較多的決策系統(tǒng)之中。針對模擬退火算法的隨機性,優(yōu)化算法產(chǎn)生新屬性集的過程,加快了算法的收斂速度�；谏鲜鲅芯砍晒�,結(jié)合中醫(yī)院的實際需求,設(shè)計了一個中藥處方分析系統(tǒng),實現(xiàn)對藥材與藥材、病證與藥材之間關(guān)系的挖掘。通過實際處方數(shù)據(jù)比較不同算法的有效性,驗證了該中藥處方分析系統(tǒng)可以提供一定的用藥指導(dǎo),并且可以提高藥房的工作效率,具有較高的實用價值。
[Abstract]:Chinese medicine is a treasure of the Chinese nation, Chinese medicine as the main support of traditional Chinese medicine, has played a huge role in Chinese medicine theory. For thousands of years, people's health problems Chinese basically is relying on Chinese medicine, eat Chinese traditional medicine to solve. With the construction of hospital information, hospital database stored in large quantities. So the prescription of Chinese medicine the rise, using data mining technology, the traditional Chinese medicine prescription and modern information processing technology, in the absence of a priori knowledge, to mine contains herbs and the relationship between the prescription rules, the research has a good application prospect. This paper introduces the classic Apriori algorithm based on association rules count distribution analysis using the theory of the improved parallel algorithm (CD algorithm). Aiming at the shortcomings of traditional algorithms, we propose parallel association rules mining algorithm based on matrix (PM algorithm), to solve the traditional serial The speed bottleneck algorithm. Compared with CD algorithm, PM algorithm can quickly generate frequent itemsets. This paper analyzes the traditional attribute reduction algorithms of rough set, through the analysis of the modeling, this paper presents a new attribute reduction algorithm based on simulated annealing algorithm, and the algorithm is applied to the decision-making system of condition attributes more. According to random simulation annealing algorithm, optimization algorithm to generate new attributes, accelerate the convergence speed of the algorithm. Based on the above research results, combined with the actual needs of the hospital, designed a prescription analysis system of medicinal materials and medicinal materials, exploring the relationship between the disease and medicine. The effectiveness of the actual data comparison of different prescription the algorithm, verified the prescription analysis system can provide a medication guide, and can improve the work efficiency of the pharmacy, it has a high practical value.

【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP311.13

【參考文獻】

相關(guān)期刊論文 前10條

1 程學(xué)旗;靳小龍;王元卓;郭嘉豐;張鐵贏;李國杰;;大數(shù)據(jù)系統(tǒng)和分析技術(shù)綜述[J];軟件學(xué)報;2014年09期

2 毛燕琴;沈蘇彬;;物聯(lián)網(wǎng)信息模型與能力分析[J];軟件學(xué)報;2014年08期

3 田有亮;彭長根;馬建峰;林輝;楊凱;;通用可組合公平安全多方計算協(xié)議[J];通信學(xué)報;2014年02期

4 王智鋼;王池社;馬青霞;;分布式并行關(guān)聯(lián)規(guī)則挖掘算法研究[J];計算機應(yīng)用與軟件;2013年10期

5 張敏;姚良威;侯宇;;基于向量和矩陣的頻繁項集挖掘算法研究[J];計算機工程與設(shè)計;2013年03期

6 孟小峰;慈祥;;大數(shù)據(jù)管理:概念、技術(shù)與挑戰(zhàn)[J];計算機研究與發(fā)展;2013年01期

7 孫秀麗;楊麗琴;于靜;;數(shù)據(jù)挖掘研究及在醫(yī)學(xué)中的應(yīng)用[J];醫(yī)學(xué)信息(上旬刊);2011年05期

8 吳尚智;茍平章;;粗糙集和信息熵的屬性約簡算法及其應(yīng)用[J];計算機工程;2011年07期

9 王建強;仲曉偉;楊飛;;數(shù)據(jù)挖掘在醫(yī)療臨床路徑中的應(yīng)用[J];現(xiàn)代醫(yī)院;2011年03期

10 李毅;保鵬飛;薛萬國;;中文電子病歷的信息抽取研究[J];生物醫(yī)學(xué)工程學(xué)雜志;2010年04期

，

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