本文關(guān)鍵詞：基于電子病歷的急性冠脈綜合征患者主要不良心血管事件預(yù)測　出處：《浙江大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： Dempster-Shafer證據(jù)理論 自然語言處理 機器學(xué)習(xí) 主要不良心血管事件預(yù)測 急性冠脈綜合征 電子病歷

【摘要】：主要不良心血管事件預(yù)測與評估是研究急性冠脈綜合征等心血管疾病致病危險因素與疾病發(fā)病率、死亡率之間數(shù)量依存關(guān)系及規(guī)律的技術(shù),被普遍認為是進行疾病防治的核心環(huán)節(jié)。預(yù)測結(jié)果能夠為醫(yī)生提供臨床決策支持,輔助醫(yī)生制定合理的治療及護理方案,從而減小患者發(fā)生不良事件的幾率;更能規(guī)范醫(yī)療流程,減少醫(yī)療開支。傳統(tǒng)隊列研究通過入組標準控制患者質(zhì)量,采用少量精選風(fēng)險因子構(gòu)建模型,使用簡單并已得到廣泛的臨床認可。但其存在如入組標準導(dǎo)致入組患者與實際臨床環(huán)境不同;少量風(fēng)險因子限制模型性能;難以納入新的風(fēng)險因子等不足。隨著電子病歷等醫(yī)療信息系統(tǒng)的快速發(fā)展,大量研究開始采用電子病歷數(shù)據(jù)構(gòu)建預(yù)測模型。相對于隊列研究,該類模型沒有嚴格的入組標準,數(shù)據(jù)反應(yīng)真實臨床環(huán)境;數(shù)據(jù)豐富,可用患者信息多;可納入新的風(fēng)險因子。盡管克服了隊列研究的不足,但依然存在如1)電子病歷數(shù)據(jù)尚未充分利用2)數(shù)據(jù)不準確值及缺失值導(dǎo)致模型不確定性大、預(yù)測結(jié)果不準確等問題。因此,本論文針對上述基于電子病歷數(shù)據(jù)預(yù)測方法的不足,提出了一種基于電子病歷數(shù)據(jù)挖掘的主要不良心血管事件預(yù)測方法。該方法主要由四部分組成:第一,在處理檢查檢驗數(shù)據(jù)同時,使用自然語言處理技術(shù)從入院記錄中提取患者特征,充分使用獲取到的電子病歷數(shù)據(jù)。第二,使用四種常用的機器學(xué)習(xí)算法,即支持向量機、隨機森林、樸素貝葉斯及范數(shù)一邏輯回歸,構(gòu)建獨立不良事件預(yù)測模型。第三,使用粗糙集理論計算各獨立不良事件預(yù)測模型的權(quán)重值,來確定其在集成模型中所應(yīng)發(fā)揮的作用。第四,采用Dempster-Shafer證據(jù)理論,將多個獨立預(yù)測模型的輸出結(jié)果和已得到廣泛臨床認可的隊列研究模型GRACE相融合,從而得到本輪文提出的集成主要不良心血管事件預(yù)測模型。通過使用從醫(yī)院收集到的2,930份急性冠脈綜合征電子病歷數(shù)據(jù)對本論文所提出的集成主要不良心血管事件預(yù)測方法進行評估。評估結(jié)果表明:1)使用自然語言處理技術(shù)深度挖掘非結(jié)構(gòu)化電子病歷數(shù)據(jù)能有效提高不良事件預(yù)測精度;2)使用Dempster-Shafer·證據(jù)理論構(gòu)建的集成預(yù)測模型在與獨立預(yù)測模型和其他集成模型對比時,取得了最佳的綜合預(yù)測性能,有效減少了電子病歷數(shù)據(jù)中不準確值及缺失值對模型預(yù)測性能產(chǎn)生的影響。
[Abstract]:The prediction and evaluation of major adverse cardiovascular events is a technique to study the quantitative relationship and regularity between risk factors and morbidity and mortality of cardiovascular diseases such as acute coronary syndrome (ACS). It is generally considered as the core link of disease prevention and treatment. The predicted results can provide doctors with clinical decision support, assist doctors to formulate reasonable treatment and nursing programs, and thus reduce the probability of adverse events. Traditional cohort studies control patient quality through group standards and use a small selection of risk factors to build models. Use is simple and has been widely recognized in clinical practice. However, the presence of such criteria leads to a difference between the patients in the group and the actual clinical environment. A small number of risk factors limit the performance of the model; With the rapid development of medical information systems such as electronic medical records, a large number of studies began to use electronic medical records data to build prediction models. This kind of model has no strict entry standard, and the data reflect the real clinical environment. Abundant data, available patient information; Although it overcomes the shortage of cohort research, it still exists such as: 1) the electronic medical record data has not been fully utilized 2) the inaccurate value and missing value of the data lead to the uncertainty of the model. Therefore, this paper aims at the shortcomings of the above methods based on EMR data prediction. A main adverse cardiovascular event prediction method based on EMR data mining is proposed. The method consists of four parts: first, it processes the inspection data at the same time. Using natural language processing technology to extract patient features from hospital records, fully use the obtained electronic medical record data. Second, use four commonly used machine learning algorithms, namely support vector machine, random forest. Naive Bayes and norm-logical regression to build independent adverse event prediction model. Third, using rough set theory to calculate the weight of each independent adverse event prediction model. To determine its role in the integration model. 4th, using Dempster-Shafer evidence theory. The output results of multiple independent predictive models are fused with GRACE, a cohort study model that has been widely accepted in clinical practice. Thus the integrated major adverse cardiovascular events prediction model proposed in this paper is obtained. 2. 930 electronic medical records of acute coronary syndrome (ACS) were used to evaluate the integrated method for predicting major adverse cardiovascular events proposed in this paper. Deep mining of unstructured EMR data with natural language processing technology can effectively improve the prediction accuracy of adverse events. 2) compared with independent prediction model and other integrated models, the integrated prediction model constructed with Dempster-Shafer 路evidence theory achieves the best comprehensive prediction performance. It can effectively reduce the influence of inaccurate and missing values on the predictive ability of the model.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R541.4

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