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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 傅立成(Li-Chen Fu) | |
dc.contributor.author | Li-Hung Yao | en |
dc.contributor.author | 姚力宏 | zh_TW |
dc.date.accessioned | 2021-06-17T06:33:07Z | - |
dc.date.available | 2023-09-01 | |
dc.date.copyright | 2021-01-14 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-09-03 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/72282 | - |
dc.description.abstract | 急診室擁擠已經成為世界各地所需面對的公共醫療問題。根據許多國家的研究,急診患者的數量不斷攀升,這導致急診醫護人員面臨了前所未有的擁擠和護理負擔,更將造成患者的延誤治療。為了解決急診擁擠問題,各國積極的發展不同的嚴重程度分類標準,其中最被廣泛使用的是緊急嚴重程度指標(ESI),該方法將患者分為五級,以利護理人員能夠依據分級來做救治的參考。儘管ESI提升了急診治療的效率,但到目前為止,該方法因過度仰賴於護理人員的主觀判斷,很容易將大多數患者分類到ESI的第三級。因此,一個可以幫助醫生準確地分類患者病情的系統是迫切需要的。在此論文中,我們提出了一個基於患者急診電子健康記錄(EMRs)的患者住院預測系統,此系統透過卷積神經網路結合遞歸神經網絡匹配聚焦機制進行分類,比起過去相關研究僅使用純結構化或純文字資料特徵,本系統可以處理更加完整的不同類型資料,並使用在不同語言當中。為了進行驗證,在美國急診醫療調查資料中,使用118,602名患者資料,精準度和AUROC分別為0.83及0.87。測試在745,441名患者的台灣資料當中,精準度和AUROC則可達到0.83和0.88。此外,為了驗證本系統在醫療領域的有效性,我們亦將其應用在其他的醫療結果預測,包含死亡率及是否需要入住ICU,相比較其他的傳統機器學習方法,我們的結果在精準度及AUROC高了3~5%。 | zh_TW |
dc.description.abstract | Emergency Department (ED) crowding has become an issue of delayed patient treatment and even a public healthcare problem around the world. According to recent research studies of many countries, the increasing number of patients in the emergency department which has led to unprecedented crowding and delays in care. For that reason, triage into five-level Emergency Severity Index (ESI) has become a major method for improving medical priorities in ED. Although the ESI mitigates the process of ED treatment, so far it still heavily relies on the nurse's subjective judgment and is easy to triage most patients to ESI level 3 in current practice. Therefore, a system that can help the doctors to accurately triage a patient's condition is imperative. In this work, we propose a system based on the patients’ ED electronic medical record to predict hospitalizations after assigned procedures in ED are completed. This system used Convolutional Neural Networks combined with Recurrent Neural Networks together with attention mechanism for classification. Compared with past related research that only used structural or textual data features, our system can process more different types of data and thus to more applicable to different countries with different languages. For verification, the data from an open dataset (National Hospital Ambulatory Medical Care Survey) is used which includes 118,602 patient visits of the United States EDs, and the accuracy and AUROC can achieve 0.83 and 0.87, respectively. On the other hand, validation on our dataset that includes 745,441 patient visits in National Taiwan University Hospital, and the accuracy and AUROC can reach 0.83 and 0.88, respectively. Moreover, in order to effectively evaluate the prediction quality of our proposed system, we also applied the model on other clinical outcomes that contain the mortality and the admission of ICU, the results showed that our method is 3~5% higher in accuracy than other common methods, including three traditional machine learning algorithms. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T06:33:07Z (GMT). No. of bitstreams: 1 U0001-0309202016460700.pdf: 3418585 bytes, checksum: 24d6f919654ba001f618463b285b2402 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 口試委員會審定書 i 誌謝 ii 中文摘要 iii ABSTRACT iv CONTENTS vi LIST OF FIGURES ix LIST OF TABLES xi Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation 3 1.3 Challenges 6 1.3.1 Combined Use of Structural and Unstructured Data is Difficult 6 1.3.2 The Analysis of NLP in the Clinical Field is Harder 7 1.3.3 To Apply Triage System on Different Languages is Hard 7 1.4 Related Work 8 1.5 Objectives 11 1.5.1 Be Able to Analyze Data Using Different Algorithms 11 1.5.2 Be Able to Completely Mix and Use Different Types of Data 11 1.5.3 Effective in Different Languages 12 1.6 Thesis Organization 12 Chapter 2 Preliminaries 13 2.1 Word Vectors 13 2.1.1 Introduction of Word Vectors 13 2.1.2 Continuous Bag of Words (CBOW) and Skip-gram 14 2.1.3 FastText 17 2.2 Recurrent Neural Networks 18 2.2.1 Networks Architecture 18 2.2.2 Backpropagation through Time 20 2.2.3 Bi-directional RNNs 24 2.2.4 Long-Term Dependency and LSTM/GRU 25 2.3 Attention Mechanism 29 2.4 Convolutional Neural Network 31 2.4.1 Network Architecture 31 2.4.2 Convolutional Layer 32 2.4.3 Pooling Layer 34 2.4.4 Fully Connected Layer 35 2.4.5 Training and Backpropagation 35 Chapter 3 System Overview 37 3.1 System Overview 37 3.2 Data Preparation 38 3.3 Problem Formulation and Description 40 3.4 Triage Engine 41 3.4.1 Module for Long Sentence (RNN-type) 43 3.4.2 Module for Short Sentence (CNN-type) 45 3.4.3 Training of Model 49 Chapter 4 System Evaluation 50 4.1 Experiment Platform 50 4.2 Data Preparation 50 4.2.1 NHAMCS Dataset 51 4.2.2 NTUH Dataset 52 4.2.3 Data Preprocessing 53 4.2.4 Transformation Process 55 4.3 Performance on NHAMCS Dataset 56 4.3.1 RNNs Part 57 4.3.2 CNNs Part 58 4.3.3 Compare to Baseline Algorithms 59 4.4 Performance on NTUH Dataset 60 4.4.1 RNNs Part 61 4.4.2 CNN Part 62 4.4.3 Comparison with Baseline Algorithms 63 4.5 Compare with Other Studies 64 4.6 Apply on Other Clinical Outcomes 65 4.6.1 For Mortality 65 4.6.2 For Admission of ICU 66 Chapter 5 Conclusions 67 5.1 Summary 67 5.2 Future Work 68 REFERENCES 69 | |
dc.language.iso | en | |
dc.title | 基於患者急診電子病歷利用深度學習之檢傷預測系統 | zh_TW |
dc.title | A System for Triage Prediction of Emergency Department Based on Electronic Medical Record Using Deep Learning | en |
dc.type | Thesis | |
dc.date.schoolyear | 109-1 | |
dc.description.degree | 碩士 | |
dc.contributor.author-orcid | 0000-0001-5194-339X | |
dc.contributor.advisor-orcid | 傅立成(0000-0002-6947-7646) | |
dc.contributor.oralexamcommittee | 陳縕儂(Yun-Nung Chen),張智星(Jyh-Shing Jang),黃建華(Chien-Hua Huang),蔡居霖(Chu-Lin Tsai) | |
dc.contributor.oralexamcommittee-orcid | 陳縕儂(0000-0003-1777-3942),張智星(0000-0002-7319-9095),黃建華(0000-0003-2981-4537),蔡居霖(0000-0003-4639-1513) | |
dc.subject.keyword | 急診室,分級檢傷,住院預測,遞歸神經網絡,卷積神經網路,深度學習, | zh_TW |
dc.subject.keyword | Emergency Department,Triage System,Convolutional Neural Network,Recurrent Neural Networks,Hospital Admission,Deep Learning, | en |
dc.relation.page | 76 | |
dc.identifier.doi | 10.6342/NTU202004205 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2020-09-04 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
顯示於系所單位: | 資訊網路與多媒體研究所 |
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