本文選題：重癥監(jiān)護(hù)室 + 生死預(yù)測��； 參考：《北京交通大學(xué)》2017年碩士論文

【摘要】：重癥監(jiān)護(hù)室(Intensive Care Unit,ICU)是現(xiàn)代醫(yī)院中對搶救患有危重病情病人的重要單元,ICU患者死亡率則是衡量ICU救治水平和服務(wù)質(zhì)量的一個重要指標(biāo)。目前臨床上已經(jīng)有多種評分系統(tǒng)用于患者的病情評估和生死預(yù)測,但這些評估系統(tǒng)均需要耗費大量的人力和財力。因此在人工智能高速發(fā)展的背景下,許多學(xué)者嘗試使用數(shù)據(jù)挖掘和機(jī)器學(xué)習(xí)方法研究ICU患者生死預(yù)測問題,并取得了一些進(jìn)展,但是目前僅限于實驗室的學(xué)術(shù)研究,距離臨床應(yīng)用仍有距離,同時使用機(jī)器學(xué)習(xí)方法進(jìn)行預(yù)測使得預(yù)測結(jié)果的解釋性較差,很難被臨床醫(yī)護(hù)人員接受。因此本文提出了一種基于FAM-CART模型的ICU患者生死預(yù)測研究方法。本文主要介紹了基于FAM-CART模型的ICU患者生死預(yù)測方法。在分析了現(xiàn)有ICU患者病情評估和生死預(yù)測方法的特點基礎(chǔ)上,首先對患者的ICU監(jiān)護(hù)信息進(jìn)行整理分析,分別采用正常值、均值和二值數(shù)據(jù)填充方法進(jìn)行數(shù)據(jù)預(yù)處理,并根據(jù)生理指標(biāo)的臨床特性對其進(jìn)行特征提取,然后采用Fuzzy ARTMAP神經(jīng)網(wǎng)絡(luò)進(jìn)行ICU患者的生死預(yù)測,并將基于三種數(shù)據(jù)預(yù)處理方法的預(yù)測結(jié)果進(jìn)行對比。最后采用預(yù)測結(jié)果最優(yōu)的數(shù)據(jù)預(yù)處理方法,利用FAM-CART模型對ICU患者的生死進(jìn)行預(yù)測,最后將預(yù)測結(jié)果與臨床評分系統(tǒng)和邏輯回歸、人工神經(jīng)網(wǎng)絡(luò)、支持向量機(jī)、Adaboost等算法的預(yù)測結(jié)果進(jìn)行比較和分析。本文主要開展了以下研究工作:(1)總結(jié)和分析臨床ICU患者生死預(yù)測方法的現(xiàn)狀和不足,從而提出基于FAM-CART模型的ICU患者生死預(yù)測的方法;(2)提出了基于混合FAM-CART模型的ICU患者生死預(yù)測方法,通過使用數(shù)據(jù)集訓(xùn)練Fuzzy ARTMAP神經(jīng)網(wǎng)絡(luò),并利用其得到的原型節(jié)點的質(zhì)心和置信因子與CART相結(jié)合,從而構(gòu)建FAM-CART模型用于ICU患者的生死預(yù)測研究;(3)通過分析ICU患者數(shù)據(jù)集的特點和缺失程度,設(shè)計三種數(shù)據(jù)預(yù)處理方法,并采用Fuzzy ARTMAP神經(jīng)網(wǎng)絡(luò)對數(shù)據(jù)預(yù)處理方法進(jìn)行驗證,確定能獲得最好預(yù)測結(jié)果的數(shù)據(jù)預(yù)處理方法;(4)采用FAM-CART模型實現(xiàn)ICU患者生死預(yù)測,并將預(yù)測結(jié)果與基于Fuzzy ARTMAP神經(jīng)網(wǎng)絡(luò)得到的預(yù)測結(jié)果,以及其它經(jīng)典的機(jī)器學(xué)習(xí)方法的預(yù)測結(jié)果進(jìn)行對比分析,驗證本研究方法的預(yù)測效果。本文研究旨在根據(jù)臨床ICU監(jiān)護(hù)數(shù)據(jù),設(shè)計一種既具有良好的預(yù)測性能,又能被臨床醫(yī)護(hù)人員理解和接受的ICU患者生死預(yù)測方法,研究結(jié)果表明論文中提出的方法能取得較好的預(yù)測性能,可以為臨床應(yīng)用提供理論參考。
[Abstract]:Intensive Care Unit (ICU) is an important unit in modern hospitals for rescuing critically ill patients. The mortality rate of ICU patients is an important index to measure the level of ICU treatment and the quality of service. At present, there are a variety of clinical scoring systems for patients to assess the disease and life and death prediction, but these assessment systems require a lot of human and financial resources. Therefore, under the background of the rapid development of artificial intelligence, many scholars try to use data mining and machine learning methods to study the problem of life and death prediction of ICU patients, and have made some progress, but only in the laboratory academic research. There is still a distance from clinical application and machine learning method is used to predict the result which is difficult to be accepted by medical staff. Therefore, this paper presents a method for predicting the life and death of ICU patients based on FAM-CART model. This paper mainly introduces the method of predicting the life and death of ICU patients based on FAM-CART model. On the basis of analyzing the characteristics of the existing methods of ICU patients' condition evaluation and life and death prediction, the information of patients' ICU monitoring is analyzed firstly, and the normal value, mean value and binary data filling method are used to preprocess the data, respectively. Then Fuzzy ARTMAP neural network was used to predict the life and death of ICU patients, and the prediction results based on three data preprocessing methods were compared. Finally, the optimal data preprocessing method is used to predict the life and death of ICU patients with FAM-CART model. Finally, the prediction results are combined with clinical scoring system and logic regression, artificial neural network. The prediction results of support vector machine and Adaboost algorithms are compared and analyzed. This article mainly carried out the following research work: 1) summarizing and analyzing the present situation and deficiency of the methods of predicting the life and death of clinical ICU patients. The method of predicting the life and death of ICU patients based on FAM-CART model is put forward. The method of predicting the life and death of ICU patients based on mixed FAM-CART model is presented. The Fuzzy ARTMAP neural network is trained by using data sets. The centroid and confidence factor of the prototype node are combined with CART to construct FAM-CART model to predict the life and death of ICU patients. By analyzing the characteristics and missing degree of ICU patient data set, three data preprocessing methods are designed. The Fuzzy ARTMAP neural network is used to verify the data preprocessing method and the data preprocessing method which can obtain the best prediction results is determined. The FAM-CART model is used to predict the life and death of ICU patients. The prediction results are compared with those based on Fuzzy ARTMAP neural network and other classical machine learning methods to verify the effectiveness of the proposed method. The purpose of this study is to design a method for predicting the life and death of ICU patients, which has good predictive performance and can be understood and accepted by medical staff according to clinical ICU monitoring data. The results show that the proposed method can achieve good predictive performance and provide theoretical reference for clinical application.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R459.7

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