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研究生:高奕昕
研究生(外文):I-Hsi Kao
論文名稱:利用深度學習預測疑似敗血症病患的死亡及新型冠狀病毒的擴散
論文名稱(外文):Predicting the Mortality of Suspected Sepsis Patient and the Pandemic Severity of COVID-19 using Deep Learning
指導教授:彭昭暐彭昭暐引用關係
指導教授(外文):Perng, Jau-woei
學位類別:博士
校院名稱:國立中山大學
系所名稱:機械與機電工程學系研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2021
畢業學年度:110
語文別:英文
論文頁數:162
中文關鍵詞:新型冠狀病毒深度學習急診室機器學習死亡率預測快速器官衰竭評估全身性炎症反應綜合徵
外文關鍵詞:COVID-19Deep learningEmergency departmentMachine learningMortality predictionqSOFASIRS
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在過去,醫生使用快速器官衰竭評估分數(qSOFA)和全身性炎症反應綜合徵(SIRS)來確定患者的生存可能性。儘管這樣的預測方法很方便,但是它們的準確性仍有提高的空間。在本論文中,三種深度學習方法作為預測患者到急診室的前72小時與28天內死亡率的有效方法被提出。為了證明本論文提出的方法的有效性與強健性,使用了來自醫院的原始數據。對2007年1月至2013年12月間疑似被感染的42,220位成年急診患者進行了整合型回顧性研究,使用患者的生命特徵和實驗室數據計算qSOFA和SIRS。在預測急診病患死亡率研究進行的同時,2019年11月,一種新型冠狀病毒爆發嚴重的急性呼吸系統綜合症疾病(COVID-19)。並且在短短兩個多月的時間裡,全世界確診病例數已超過10,000,展現出空前的迅速傳播。本論文利用深度學習的預測方法對於COVID-19進行分析研究。
本論文主要探討深度學習在醫療領域中的預測性能。深度信念網路、卷積神經網路、自編碼器、卷積自編碼器、長短期記憶等多種神經網路在本論文中被實現。所預測的目標包含病人在急診室中的死亡率,以及COVID-19在美國的擴散情形。並且,一種全新的混合式神經網路AL-CNN在本論文首次被提出,以此實現COVID-19在美國的擴散情形。本論文不只利用深度學習的架構進行目標預測,隨機森林、K-近鄰演算法、支持向量機、主成分分析等機器學習方法也被探討其與深度學習的效果與差異。
Previously, in order to define the survival probability of infected patients, researchers used the qSOFA and SIRS to calculate the mortality scores. This dissertation proposes three deep learning methods for predicting the mortality of patients within 72 h and 28 d. A retroactive single-center study was performed on adult patients (under 18) that were suspected to be inflected between Jan. 2007 and Dec. 2013, in the ED. While studies on predicting the mortality of emergency patients were underway, in Nov. 2019, the novel severe acute respiratory syndrome coronavirus 2 outbreak began and wreak havoc on the world. In only two months, this outbreak showed an unparalleled rapid spread resulting in more than 10 thousand confirmed diagnoses cases, globally. In this dissertation, the prediction method of deep learning was applied to analyze the spread of the novel severe acute respiratory syndrome coronavirus 2, which is also knowns as coronavirus or COVID-19.
This dissertation discussed the prediction performance of deep learning in medical settings. A variety of neural networks, such as Deep Belief Network, Convolutional Neural Network, autoencoder, Convolutional Autoencoder, and Long Short-Term Memory were investigated in this dissertation, and their architectures are used to predict medical goals. The predicted goals included the mortality of patients in the ED and the spread of coronavirus in the United States. A new neural network architecture, AL-CNN is presented in this work. Furthermore, according to present comparisons, several machine learning methods have also been explored for their effects and differences with deep learning.
論文審定書 i
致謝 ii
Acknowledgement x
摘要 xxi
Abstract xxii
Contents xxiii
List of Figures xxviii
List of Tables xxx
List of Appendices xxxii
Chapter 1 Introduction 1
1.1 Motivation 1
1.1.1 Mortality Prediction in the Emergency Department 1
1.1.2 Prediction of the Severity of the Coronavirus Disease Pandemic 4
1.2 Paper Review 7
1.2.1 Artificial Intelligence of Clinical Medicine 7
1.2.2 Prediction of Pandemic 8
1.3 Contribution and Organization of the Dissertation 13
Chapter 2 A Brief Review of Methods 14
2.1 The SIRS, SOFA, and qSOFA 14
2.1.1 The SIRS 15
2.1.2 The SOFA 16
2.1.3 The qSOFA 18
2.2 Machine Learning Methods Applied in The Dissertation 19
2.2.1 Principal Component Analysis 19
2.2.2 Support Vector Regression 20
2.2.3 Random Forest Regression 22
2.2.4 K Nearest Neighbor 24
2.3 Deep Learning Methods Applied in The Dissertation 26
2.3.1 The Deep Belief Network 26
2.3.2 Autoencoder 29
2.3.3 Convolutional Neural Network 31
2.3.4 Long Short-Term Memory 34
Chapter 3 72 h Mortality Prediction Using a Deep Belief Network with 65 Features in the Emergency Department 37
3.1 Data & Data Pre-processing 37
3.2 The Design of the Deep Belief Network 41
3.3 The Experiment Results 42
3.3.1 Feature Extraction of the Deep Belief Network 42
3.3.2 Receiver Operating Characteristic & Accuracy Rate 43
3.3.3 The Importance Features 46
3.4 Computation Tools 48
Chapter 4 72 h & 28 d Mortality Prediction Using Machine Learning with 53 Features in the Emergency Department 49
4.1 Data & Data-preprocessing 49
4.2 The Design of a Deep Learning Constructure 51
4.2.1 The Design of an Autoencoder 51
4.2.2 The Design of Convolutional Neural Networks 53
4.3 The Experiment Results 56
4.3.1 Feature Extraction 56
4.3.2 Receiver Operating Characteristic & Accuracy Rate 57
4.3.3 Importance Features 65
4.4 Computation Tools 67
Chapter 5 Early Prediction of the COVID-19 Pandemic Level Using an AL-CNN 68
5.1 Data & Data Pre-processing 68
5.1.1 Data 68
5.1.2 Pre-processing 69
5.2 The Design of Deep Learning 72
5.2.1 The Design of the Convolutional Autoencoder 72
5.2.2 The Designed of AL-CNN 74
5.3 The Experiment Results 76
5.3.1 Prediction Results 76
5.3.2 Feature Observation 81
5.4 Computation Tools 84
Chapter 6 Early Prediction of COVID-19 Pandemic Level with Climate Data 85
6.1 Data & Data Pre-processing 85
6.1.1 Data of COVID-19 85
6.1.2 Data of Climate 86
6.1.3 Data Pre-processing 86
6.2 The Design of Deep Learning 88
6.2.1 The Design of Deep Learning Constructure without Climate Information 88
6.2.2 The Design of Deep Learning Constructure with Climate Information 90
6.3 The Experiment Results 93
6.3.1 Prediction Results 93
6.3.2 Feature Visualization 95
6.4 Computation System 97
Chapter 7 Conclusions and Future Works 98
7.1 Conclusions 98
7.2 Future Studies 100
References 102
Appendices 116
Resume 118
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