本文選題：醫(yī)療大數(shù)據(jù) + 醫(yī)療輔助診斷��； 參考：《東華大學(xué)》2016年碩士論文

【摘要】：我國醫(yī)院大范圍建設(shè)以電子病歷為重點的信息系統(tǒng)已進行了10多年。電子病歷、實驗室信息系統(tǒng)、醫(yī)學(xué)影像傳輸和存儲系統(tǒng)等信息系統(tǒng)已被引入到醫(yī)院中,這使得醫(yī)院積累了豐富的醫(yī)療數(shù)據(jù)資源。以上海交通大學(xué)醫(yī)學(xué)院附屬瑞金醫(yī)院為例,每年產(chǎn)生的臨床數(shù)據(jù)約在60TB。醫(yī)療數(shù)據(jù)包括了臨床診斷、檢驗檢查結(jié)果等類型,具有數(shù)據(jù)量大、數(shù)據(jù)形式多元、數(shù)據(jù)變化快、數(shù)據(jù)價值高的“大數(shù)據(jù)”特征。如何利用這些數(shù)據(jù)為臨床醫(yī)療診斷提供數(shù)據(jù)支持、讓醫(yī)生更好地為病人服務(wù),是信息化建設(shè)的更高要求。論文結(jié)合《基于瑞金醫(yī)院的臨床大數(shù)據(jù)平臺建設(shè)和深度應(yīng)用》實際項目,研究了基于大數(shù)據(jù)處理平臺Hadoop的醫(yī)療輔助診斷系統(tǒng)。首先分析了醫(yī)療數(shù)據(jù)的特點,綜述了數(shù)據(jù)挖掘相關(guān)技術(shù)。針對糖尿病人處方數(shù)據(jù),研究了基于關(guān)聯(lián)規(guī)則的藥品推薦,并通過改進算法提高效率滿足了醫(yī)療診斷的及時性要求。針對甲亢病人的檢驗數(shù)據(jù),研究了基于邏輯回歸的并發(fā)癥預(yù)測,結(jié)合醫(yī)療數(shù)據(jù)特點,分析了數(shù)據(jù)清洗、集成、轉(zhuǎn)換以及預(yù)測結(jié)果的評估相關(guān)技術(shù)。使用MapReduce和Spark兩種計算模型,論文對醫(yī)療輔助診斷系統(tǒng)的藥品推薦和并發(fā)癥預(yù)測功能進行了實現(xiàn),驗證了課題研究方法和技術(shù)的有效性。論文的主要工作有以下幾個方面。(1)研究了基于關(guān)聯(lián)規(guī)則的藥物推薦。利用數(shù)據(jù)挖掘中的關(guān)聯(lián)規(guī)則算法,找出醫(yī)療處方數(shù)據(jù)中的頻繁處方項集,并從頻繁處方項集中尋找有意義的處方關(guān)聯(lián)信息,在醫(yī)生開具處方時提供輔助診斷作用。為了滿足藥品推薦的及時性需求,論文對關(guān)聯(lián)規(guī)則的經(jīng)典Apriori算法進行改進,提出基于前綴項集的Apriori算法,并在實際醫(yī)療處方數(shù)據(jù)上證明了改進算法的有效性。(2)研究了基于邏輯回歸的并發(fā)癥預(yù)測。針對病人的各種檢驗指標(biāo)數(shù)據(jù),結(jié)合數(shù)據(jù)挖掘中的邏輯回歸算法建立分類模型,通過該模型對未來病人的患病情況進行判斷,從而達到為醫(yī)生提供輔助診斷的效果。在預(yù)測功能具體實現(xiàn)中,本文使用了1～99分位極值處理、基于熵的數(shù)值離散化、針對不平衡數(shù)據(jù)的模型評估方法等技術(shù)以保證最終預(yù)測模型的準(zhǔn)確性。(3)在Hadoop平臺上使用MapReduce計算模型,實現(xiàn)了藥品推薦功能,并通過實驗比較分析了經(jīng)典和改進的Apriori算法的性能,驗證了改進算法的有效性；使用Spark計算模型,實現(xiàn)了并發(fā)癥預(yù)測功能,并以原發(fā)性甲狀腺機能亢進癥病癥為例,詳細(xì)分析了功能的實現(xiàn)過程,給出了預(yù)測結(jié)果。
[Abstract]:It has been more than 10 years since the construction of electronic medical records information system in Chinese hospitals. Electronic medical records, laboratory information systems, medical image transmission and storage systems have been introduced into hospitals, which makes hospitals accumulate rich medical data resources. Take Ruijin Hospital affiliated to the School of Medicine of Shanghai Jiaotong University as an example. The annual clinical data are about 60 TB. Medical data includes clinical diagnosis, test results and other types. It has the characteristics of large amount of data, multiform data, fast data change and high data value. How to make use of these data to provide data support for clinical medical diagnosis and to make doctors better serve patients is a higher requirement of information construction. Based on the practical project of "Clinical big data platform Construction and Deep Application based on Ruijin Hospital", a medical assistant diagnosis system based on big data processing platform is studied. Firstly, the characteristics of medical data are analyzed, and the related technologies of data mining are summarized. Based on the prescription data of diabetic patients, the drug recommendation based on association rules was studied, and the improved algorithm was used to improve the efficiency to meet the requirement of timely medical diagnosis. Based on the test data of hyperthyroidism patients, the prediction of complications based on logical regression was studied. Combined with the characteristics of medical data, the related techniques of data cleaning, integration, conversion and evaluation of predicted results were analyzed. By using MapReduce and Spark models, this paper realizes the function of drug recommendation and complication prediction of medical assistant diagnosis system, and verifies the effectiveness of the research method and technology. The main work of this paper is as follows: 1) Research on drug recommendation based on association rules. The association rules algorithm in data mining is used to find out the frequent prescription item set in the medical prescription data, and to search for meaningful prescription association information from the frequent prescription item set, and to provide the assistant diagnosis function when the doctor prescribes the prescription. In order to meet the timely demand of drug recommendation, this paper improves the classical Apriori algorithm of association rules, and proposes a Apriori algorithm based on prefix itemsets. The effectiveness of the improved algorithm is proved by the actual medical prescription data.) the complication prediction based on logical regression is studied. Based on the data of various test indexes of patients and the logical regression algorithm in data mining, a classification model is established, which is used to judge the disease situation of patients in the future, so as to provide assistant diagnosis for doctors. In the realization of the prediction function, the maximum value of 1 ~ 99 quartile is used in this paper, and the numerical value is discretized based on entropy. In order to ensure the accuracy of the final prediction model, the MapReduce computing model is used on the Hadoop platform to realize the drug recommendation function. The performance of the classical and improved Apriori algorithm is compared and analyzed through experiments. The effectiveness of the improved algorithm is verified, and the function of complication prediction is realized by using Spark calculation model. Taking the primary hyperthyroidism as an example, the realization process of the function is analyzed in detail, and the prediction results are given.
【學(xué)位授予單位】：東華大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP311.13

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