本文選題：醫(yī)藥服務(wù) + 文本相似度�。� 參考：《廣東技術(shù)師范學(xué)院》2017年碩士論文

【摘要】：網(wǎng)絡(luò)日益成為醫(yī)藥咨詢、藥品傳遞與相關(guān)交易的重要場所。但醫(yī)藥電商平臺(tái)上的各種商品數(shù)據(jù)、銷售量數(shù)據(jù)以及醫(yī)療服務(wù)數(shù)據(jù)相對獨(dú)立。為整合醫(yī)藥電商行業(yè)產(chǎn)生各獨(dú)立數(shù)據(jù),發(fā)掘埋藏在數(shù)據(jù)之下潛在的有價(jià)值的信息,滿足醫(yī)藥電商行業(yè)日益增長的大數(shù)據(jù)需求。本文給出了構(gòu)建醫(yī)藥大數(shù)據(jù)服務(wù)系統(tǒng)的方案,并為醫(yī)藥電商提供藥品推薦和銷售預(yù)測服務(wù)。首先,本文介紹了醫(yī)藥大數(shù)據(jù)服務(wù)系統(tǒng)的構(gòu)成。該系統(tǒng)是由醫(yī)藥大數(shù)據(jù)采集、醫(yī)藥數(shù)據(jù)分析平臺(tái)以及報(bào)表呈現(xiàn)三個(gè)子系統(tǒng)構(gòu)成。醫(yī)藥大數(shù)據(jù)采集子系統(tǒng)負(fù)責(zé)數(shù)據(jù)的采集并將數(shù)據(jù)推送給大數(shù)據(jù)分析平臺(tái)。數(shù)據(jù)分析子系統(tǒng)負(fù)責(zé)將采集到的數(shù)據(jù)運(yùn)用推薦算法和預(yù)測算法進(jìn)行分析,實(shí)現(xiàn)藥品推薦和藥品銷售量預(yù)測的特色功能。報(bào)表呈現(xiàn)子系統(tǒng)則負(fù)責(zé)將分析的結(jié)果以可視化的形式呈現(xiàn)出來。其次,本文對文本相似度計(jì)算相關(guān)技術(shù)進(jìn)行研究,在對藥品文本進(jìn)行預(yù)處理時(shí)考慮到實(shí)際數(shù)據(jù)的特點(diǎn)對文本的分詞結(jié)果進(jìn)行特征詞提取,降低了數(shù)據(jù)的維度,提高了計(jì)算結(jié)果的精確度。并結(jié)合藥品說明文本特征詞特點(diǎn),對傳統(tǒng)空間向量模型在文本相似度計(jì)算時(shí)無法體現(xiàn)特征項(xiàng)所具有的表現(xiàn)能力進(jìn)行改進(jìn)。實(shí)驗(yàn)結(jié)果表明,改進(jìn)后的空間向量模型提高了藥品之間的相似度以及藥品推薦的精確度。最后,本文對時(shí)間序列預(yù)測模型相關(guān)技術(shù)進(jìn)行研究,詳細(xì)介紹了ARIMA及GM(1,1)模型。為尋求更為精確、有效的預(yù)測方法,用基于GM(1,1)模型對ARIMA模型的殘差進(jìn)行修正,對ARIMA模型進(jìn)行優(yōu)化。實(shí)驗(yàn)結(jié)果表明,優(yōu)化后的GM-ARIMA模型達(dá)到了提高模型預(yù)測精度的目的。
[Abstract]:Network is increasingly becoming an important place for medical consultation, drug delivery and related transactions. But the various commodity data, the sales volume data and the medical service data on the pharmaceutical e-commerce platform are relatively independent. In order to integrate the pharmaceutical e-commerce industry to generate independent data, explore the potential information buried under the data, meet the growing demand of big data. This paper gives the scheme of constructing pharmaceutical big data service system, and provides drug recommendation and sales prediction service for pharmaceutical e-commerce. First of all, this paper introduces the composition of medical big data service system. The system is composed of three subsystems: medical big data collection, medical data analysis platform and report presentation. The medical big data collection subsystem is responsible for data collection and pushing the data to big data analysis platform. The data analysis subsystem is responsible for analyzing the collected data using recommendation algorithm and prediction algorithm to realize the characteristic function of drug recommendation and drug sales prediction. The report presentation subsystem is responsible for visualizing the results of the analysis. Secondly, the text similarity calculation technology is studied in this paper. In the process of drug text preprocessing, the feature word extraction of the text segmentation results is carried out considering the characteristics of the actual data, which reduces the dimension of the data. The accuracy of the calculation results is improved. Combined with the characteristics of drug description text feature words, the traditional space vector model can not reflect the expressive ability of feature items in text similarity calculation. The experimental results show that the improved spatial vector model improves the similarity between drugs and the accuracy of drug recommendation. Finally, the correlation technology of time series prediction model is studied in this paper, and the ARIMA and GM1 / 1) models are introduced in detail. In order to find a more accurate and effective prediction method, the residual error of ARIMA model is modified based on GM-1) model, and the ARIMA model is optimized. The experimental results show that the optimized GM-ARIMA model can improve the prediction accuracy of the model.
【學(xué)位授予單位】：廣東技術(shù)師范學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP311.13;TP391.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 肖蒲;賈磊;;醫(yī)藥行業(yè)大數(shù)據(jù)應(yīng)用分析[J];現(xiàn)代商業(yè);2016年20期

2 劉斌;閆佳;陳興;;淺談大數(shù)據(jù)下的Hadoop分布式架構(gòu)[J];電腦知識(shí)與技術(shù);2016年01期

3 陳磊;席曉宇;茅寧瑩;;大數(shù)據(jù)平臺(tái)在醫(yī)藥大健康行業(yè)中的應(yīng)用研究[J];科技與創(chuàng)新;2016年02期

4 杜江;張錚;張杰鑫;邰銘;;MapReduce并行編程模型研究綜述[J];計(jì)算機(jī)科學(xué);2015年S1期

5 韓冬煦;常寶寶;;中文分詞模型的領(lǐng)域適應(yīng)性方法[J];計(jì)算機(jī)學(xué)報(bào);2015年02期

6 李學(xué)龍;龔海剛;;大數(shù)據(jù)系統(tǒng)綜述[J];中國科學(xué):信息科學(xué);2015年01期

7 梁喜濤;顧磊;;中文分詞與詞性標(biāo)注研究[J];計(jì)算機(jī)技術(shù)與發(fā)展;2015年02期

8 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(bào);2014年09期

9 徐濤;于洪志;加羊吉;;基于改進(jìn)卡方統(tǒng)計(jì)量的藏文文本表示方法[J];計(jì)算機(jī)工程;2014年06期

10 孫大為;張廣艷;鄭緯民;;大數(shù)據(jù)流式計(jì)算:關(guān)鍵技術(shù)及系統(tǒng)實(shí)例[J];軟件學(xué)報(bào);2014年04期

相關(guān)碩士學(xué)位論文 前1條

1 岳莉莉;基于時(shí)間序列分析的風(fēng)速短期預(yù)測方法研究[D];華北電力大學(xué);2012年

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