發(fā)布時(shí)間：2018-05-15 01:06

  本文選題：重癥監(jiān)護(hù)室 + 生死預(yù)測(cè)��； 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(duì)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(gè)重要指標(biāo)。目前臨床上已經(jīng)有多種評(píng)分系統(tǒng)用于患者的病情評(píng)估和生死預(yù)測(cè),但這些評(píng)估系統(tǒng)均需要耗費(fèi)大量的人力和財(cái)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(cè)問(wèn)題,并取得了一些進(jìn)展,但是目前僅限于實(shí)驗(yàn)室的學(xué)術(shù)研究,距離臨床應(yīng)用仍有距離,同時(shí)使用機(jī)器學(xué)習(xí)方法進(jìn)行預(yù)測(cè)使得預(yù)測(cè)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(cè)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(cè)方法。在分析了現(xiàn)有ICU患者病情評(píng)估和生死預(yù)測(cè)方法的特點(diǎn)基礎(chǔ)上,首先對(duì)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(duì)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(cè),并將基于三種數(shù)據(jù)預(yù)處理方法的預(yù)測(cè)結(jié)果進(jìn)行對(duì)比。最后采用預(yù)測(cè)結(jié)果最優(yōu)的數(shù)據(jù)預(yù)處理方法,利用FAM-CART模型對(duì)ICU患者的生死進(jìn)行預(yù)測(cè),最后將預(yù)測(cè)結(jié)果與臨床評(píng)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(cè)結(jié)果進(jìn)行比較和分析。本文主要開(kāi)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(cè)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(cè)的方法;(2)提出了基于混合FAM-CART模型的ICU患者生死預(yù)測(cè)方法,通過(guò)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(diǎn)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(cè)研究;(3)通過(guò)分析ICU患者數(shù)據(jù)集的特點(diǎn)和缺失程度,設(shè)計(jì)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(duì)數(shù)據(jù)預(yù)處理方法進(jìn)行驗(yàn)證,確定能獲得最好預(yù)測(cè)結(jié)果的數(shù)據(jù)預(yù)處理方法;(4)采用FAM-CART模型實(shí)現(xiàn)ICU患者生死預(yù)測(cè),并將預(yù)測(cè)結(jié)果與基于Fuzzy ARTMAP神經(jīng)網(wǎng)絡(luò)得到的預(yù)測(cè)結(jié)果,以及其它經(jīng)典的機(jī)器學(xué)習(xí)方法的預(yù)測(cè)結(jié)果進(jìn)行對(duì)比分析,驗(yàn)證本研究方法的預(yù)測(cè)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(jì)一種既具有良好的預(yù)測(cè)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(cè)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(cè)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(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：R459.7

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 鄒瑜;帥仁俊;;基于改進(jìn)的SOM神經(jīng)網(wǎng)絡(luò)的醫(yī)學(xué)圖像分割算法[J];計(jì)算機(jī)工程與設(shè)計(jì);2016年09期

2 宮能凱;李倩;;常用危重癥評(píng)分在臨床應(yīng)用的研究進(jìn)展[J];右江民族醫(yī)學(xué)院學(xué)報(bào);2015年06期

3 陸雙雙;李瑩;吳莉莉;;APACHE評(píng)分系統(tǒng)的應(yīng)用及進(jìn)展[J];東南國(guó)防醫(yī)藥;2015年04期

4 王斯藤;唐旭晟;陳丹;;基于模糊自適應(yīng)共振理論映射算法的單樣本三維人臉識(shí)別[J];計(jì)算機(jī)應(yīng)用;2014年09期

5 張遠(yuǎn)健;徐健鋒;涂敏;黃學(xué)堅(jiān);劉清;;混合多機(jī)器學(xué)習(xí)的ICU病人生死預(yù)測(cè)框架[J];計(jì)算機(jī)科學(xué)與探索;2014年11期

6 李淑嫻;張淇釧;謝燦茂;;成人重癥監(jiān)護(hù)病房患者疾病嚴(yán)重程度評(píng)分系統(tǒng)的進(jìn)展[J];中國(guó)基層醫(yī)藥;2012年07期

7 王華東;曹文杰;;重癥監(jiān)護(hù)病房(ICU)特點(diǎn)及要求[J];中國(guó)社區(qū)醫(yī)師(醫(yī)學(xué)專(zhuān)業(yè));2012年07期

8 劉大為;;中國(guó)重癥醫(yī)學(xué)30年發(fā)展之路[J];中國(guó)實(shí)用內(nèi)科雜志;2011年11期

9 張仲明;于明光;郭東偉;;基于聚類(lèi)的神經(jīng)網(wǎng)絡(luò)規(guī)則抽取算法[J];吉林大學(xué)學(xué)報(bào)(信息科學(xué)版);2010年05期

10 匡胤;;基于人工神經(jīng)網(wǎng)絡(luò)的系統(tǒng)建模及MATLAB實(shí)現(xiàn)[J];四川理工學(xué)院學(xué)報(bào)(自然科學(xué)版);2007年05期

相關(guān)碩士學(xué)位論文 前3條

1 張遠(yuǎn)健;多粒度時(shí)間序列及其在ICU醫(yī)學(xué)預(yù)測(cè)應(yīng)用的研究[D];南昌大學(xué);2015年

2 郭曉亮;基于Fuzzy ARTMAP的P2P流量識(shí)別方法研究[D];重慶大學(xué);2010年

3 胡江洪;基于決策樹(shù)的分類(lèi)算法研究[D];武漢理工大學(xué);2006年

，

本文編號(hào)：1890270