本文選題：藥品需求量預(yù)測 + 數(shù)據(jù)挖掘　； 參考：《東北大學(xué)》2014年碩士論文

【摘要】：隨著疾病種類的增多,藥品的供求關(guān)系及流通環(huán)節(jié)越來越復(fù)雜,藥品用量驟增或驟減的情況也越來越頻繁,致使醫(yī)院藥品庫存頻繁出現(xiàn)供不應(yīng)求,或庫存積壓和浪費(fèi)等現(xiàn)象,因此如何準(zhǔn)確地掌握醫(yī)院藥品的需求規(guī)律,從而準(zhǔn)確預(yù)測藥品的使用量是醫(yī)院管理者迫切需要解決的現(xiàn)實(shí)問題。另一方面,隨著醫(yī)院管理信息化的深入發(fā)展,積累了大量的醫(yī)院藥品出庫和使用數(shù)據(jù),這些數(shù)據(jù)以病理為依據(jù),以處方的形式反映了病人的病況、醫(yī)生的用藥習(xí)慣、開具的藥品品種、數(shù)量及藥品之間的相互關(guān)系。如何充分利用這些歷史數(shù)據(jù),采用數(shù)據(jù)挖掘技術(shù),發(fā)現(xiàn)藥品配伍及用量之間的知識(shí)、規(guī)律和模式,基于這些模式和知識(shí)進(jìn)行藥品需求量的預(yù)測,特別是基于用藥關(guān)聯(lián)性的醫(yī)院藥品需求量預(yù)測,已經(jīng)成為醫(yī)院管理者面臨的難題,也是管理者普遍關(guān)注的研究熱點(diǎn)。本課題正是在這一背景下提出的,它是是將需求量分析從單純的時(shí)序分析邁向結(jié)合了藥品內(nèi)在關(guān)聯(lián)分析的因果預(yù)測的大膽嘗試。本文應(yīng)用數(shù)據(jù)挖掘技術(shù),從藥品出庫數(shù)據(jù)的時(shí)序性和藥品藥理的關(guān)聯(lián)性兩個(gè)角度出發(fā),通過對大連某三級(jí)甲等醫(yī)院的實(shí)地調(diào)研所獲得的數(shù)據(jù)進(jìn)行分析和建模來預(yù)測藥品的需求量,具體研究內(nèi)容包括以下幾個(gè)方面：首先,在熟悉藥品需求量預(yù)測問題研究方法的文獻(xiàn)綜述基礎(chǔ)上,從提取樣本潛在時(shí)序性信息的角度,以求和自回歸移動(dòng)平均(ARIMA)模型為基礎(chǔ),同時(shí)考慮到ARIMA模型在非線性特征上的不足以及BP神經(jīng)網(wǎng)絡(luò)模型優(yōu)秀的非線性關(guān)系學(xué)習(xí)能力,構(gòu)建了引入組合思想的ARIMA-BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型,并通過實(shí)例測試驗(yàn)證模型的實(shí)用性；然后,從提取樣本潛在關(guān)聯(lián)性信息的角度,考慮影響目標(biāo)藥品出庫規(guī)律的因素,如醫(yī)生用藥習(xí)慣、藥品的藥理性質(zhì)的影響,構(gòu)建因果關(guān)系預(yù)測模型。為完成這一工作,首先從藥品的處方數(shù)據(jù)著手,應(yīng)用數(shù)據(jù)挖掘技術(shù)Apriori算法分析藥品出庫規(guī)律之間的關(guān)聯(lián)性,旨在為后續(xù)的因果關(guān)系預(yù)測模型提供基礎(chǔ)數(shù)據(jù)；然后以BP神經(jīng)網(wǎng)絡(luò)模型為基礎(chǔ),引入遺傳算法對BP神經(jīng)網(wǎng)絡(luò)模型的初始權(quán)值和閥值進(jìn)行優(yōu)化,構(gòu)建GA-BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型,并通過實(shí)例測試驗(yàn)證模型的實(shí)用性；最后,從同時(shí)提取樣本潛在時(shí)序性信息和關(guān)聯(lián)性信息的角度,為了進(jìn)一步提高藥品需求量預(yù)測模型的準(zhǔn)確度,充分利用樣本數(shù)據(jù)的信息,引入組合預(yù)測方法的思想,以ARIMA-BP模型和GA-BP模型的預(yù)測數(shù)據(jù)為基礎(chǔ),將兩個(gè)模型的預(yù)測結(jié)果進(jìn)行組合,構(gòu)建基于GA-BP神經(jīng)網(wǎng)絡(luò)算法的智能非線性組合預(yù)測模型,并通過實(shí)例測試與模型比較驗(yàn)證模型的實(shí)用性。
[Abstract]:With the increase of disease types, the relationship between supply and demand of drugs and their circulation are becoming more and more complicated, and the situation of sudden increase or sharp decrease in drug consumption is becoming more and more frequent. As a result, the supply of drugs in hospitals frequently exceeds the supply, or the overstocking and wasting of the stocks occur frequently. Therefore, how to accurately grasp the law of hospital drug demand and accurately predict the use of drugs is a practical problem that hospital administrators urgently need to solve. On the other hand, with the further development of hospital management information, a large number of hospital drug delivery and use data have been accumulated. These data are based on pathology and reflect the patient's condition and doctors' drug use habits in the form of prescriptions. The variety, quantity and interrelation of prescribed drugs. How to make full use of these historical data and adopt data mining technology to find the knowledge, rules and patterns between drug compatibility and dosage, and based on these patterns and knowledge to predict the demand for drugs, Especially, the prediction of hospital drug demand based on drug use relevance has become a difficult problem for hospital administrators, and it is also a research hotspot that managers generally pay attention to. It is a bold attempt to change demand analysis from simple time series analysis to causality prediction combining drug intrinsic correlation analysis. In this paper, the data mining technology is used to analyze the timing of the data and the correlation of pharmaceutical pharmacology. Through the analysis and modeling of the data obtained from the field investigation of a certain Grade 3A hospital in Dalian to predict the demand for drugs, the specific research contents include the following aspects: first, Based on the literature review of the methods of drug demand prediction, and from the point of view of extracting samples' potential temporal information, the sum autoregressive moving average (ARIMA) model is used as the basis. At the same time, considering the lack of nonlinear characteristics of ARIMA model and the excellent learning ability of BP neural network model, the prediction model of ARIMA-BP neural network with combination idea is constructed, and the practicability of the model is verified by an example. Then, from the angle of extracting samples' potential relevance information, considering the factors that affect the law of target drugs' exit, such as doctors' drug usage and pharmacological properties, a causality prediction model is constructed. In order to complete this work, the correlation between drug exiting rules is analyzed by using the data mining technique Apriori algorithm, which aims at providing basic data for the subsequent causality prediction model. Then on the basis of BP neural network model, genetic algorithm is introduced to optimize the initial weight and threshold value of BP neural network model, and the prediction model of GA-BP neural network is constructed, and the practicability of the model is verified by an example. In order to further improve the accuracy of drug demand forecasting model and make full use of the information of sample data, the idea of combination forecasting method is introduced from the angle of simultaneously extracting samples' potential time series information and correlation information, in order to further improve the accuracy of drug demand forecasting model. Based on the prediction data of ARIMA-BP model and GA-BP model, the prediction results of the two models are combined to construct the intelligent nonlinear combination prediction model based on the GA-BP neural network algorithm. The practicability of the model is verified by an example test and a comparison of the model.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：R95;TP183

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 單慧亭;楊梅英;李力;沈季元;王建華;;利用移動(dòng)平均法原理設(shè)計(jì)藥品采購方案[J];中國衛(wèi)生經(jīng)濟(jì);2013年10期

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本文編號(hào)：1821373