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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 賴飛羆(Fei-Pei Lai) | |
| dc.contributor.author | Yu-Chieh Cheng | en |
| dc.contributor.author | 鄭郁潔 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:15:35Z | - |
| dc.date.available | 2021-11-06 | |
| dc.date.available | 2022-11-24T03:15:35Z | - |
| dc.date.copyright | 2021-11-06 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-13 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80760 | - |
| dc.description.abstract | "慢性肺阻塞病 (Chronic obstructive pulmonary disease, COPD) 於2020年世界衛生組織發布之全球十大死因第三名，每年奪走300萬人性命，是最嚴重的慢性疾病之一，該疾病導致呼吸困難、濃痰產生、嚴重咳嗽等症狀，患者飽受病痛之苦，生活品質不堪，其中急性發作更是造成肺功能迅速下降、致死率節節攀升的元兇。 本研究的主要目的是：（1）為慢性肺阻塞病患以及醫護人員發展一個智慧型手機遠端醫療輔助應用程式，幫助監測病患之健康狀態，及早發現COPD急性發作，（2）監測病患肺復原運動之健康狀態，並提供醫師一平台給予病患對應之回饋。此應用程式有助於病患自我督促進行肺復原運動，藉由醫師的監測，提供病患一個可安全遵從的訓練指標，使得病患更願意進行肺復原運動且在活動力指標也表現得較好。而急性發作預測模型則是使用生理數據、環境資料作為參數，並利用機器學習、深度學習建構而成。本次研究使用之模型有：隨機森林、決策樹及深度神經網路，其結果最好可達到準確度0.82、靈敏度0.53、精確性0.52，我們相信隨著資料搜集的廣泛度、多樣性，勢必能使得模型表現更加提升。 " | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:15:35Z (GMT). No. of bitstreams: 1 U0001-1310202111282600.pdf: 3464075 bytes, checksum: a16ddc5c824f2a41fb711e7568beb9be (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員會審定書 2 致謝 3 中文摘要 4 Abstract 5 Contents 7 List of figures 9 List of tables 9 1 Introduction 11 1.1 Background 11 1.2 Previous Work 13 1.3 Aim of this Study 13 2 Related Researches 14 2.1 Technology Acceptance Model (TAM) 14 2.2 Machine Learning Model 15 2.2.1 Decision Tree 15 2.2.2 Random Forest 17 3 Method 18 3.1 Data Collection 18 3.2 Smartphone Application System Architecture 20 3.3 Smartphone Application Evaluating 22 3.4 Data Preprocessing and Data Labeling 23 3.5 Classification model 26 4 Result 30 4.1 Patient Characteristics 30 4.2 Research architecture 32 4.3 COPD smartphone application 35 4.4 Exercise Indicators during Pulmonary Rehabilitation 37 4.5 Performance of AECOPD prediction models 39 4.6 Feature Importance 42 4.7 Health outcome 44 4.8 Participant Satisfaction 45 5 Discussion 48 5.1 Principal Findings 48 5.2 Comparison with Prior Work 48 5.3 Limitations 50 6 Conclusion and Future Work 51 Reference 52 | |
| dc.language.iso | en | |
| dc.subject | 穿戴式裝置 | zh_TW |
| dc.subject | 慢性肺阻塞病 | zh_TW |
| dc.subject | 肺復原運動 | zh_TW |
| dc.subject | 醫病互動平台 | zh_TW |
| dc.subject | 急性發作預測 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | pulmonary rehabilitation | en |
| dc.subject | wearable devices | en |
| dc.subject | machine learning | en |
| dc.subject | acute exacerbation prediction | en |
| dc.subject | physician-patient Interacting platform | en |
| dc.subject | deep learning | en |
| dc.subject | COPD | en |
| dc.title | 智慧手機程式應用於慢性肺阻塞病患肺功能復原訓練 | zh_TW |
| dc.title | Application of Smartphone App to Lung Function Recovery Training for Patients with Chronic Obstructive Pulmonary Disease | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 簡榮彥(Hsin-Tsai Liu),江岱倫(Chih-Yang Tseng),郭律成,顏廷聿 | |
| dc.subject.keyword | 慢性肺阻塞病,肺復原運動,醫病互動平台,急性發作預測,機器學習,深度學習,穿戴式裝置, | zh_TW |
| dc.subject.keyword | COPD,pulmonary rehabilitation,physician-patient Interacting platform,acute exacerbation prediction,machine learning,deep learning,wearable devices, | en |
| dc.relation.page | 59 | |
| dc.identifier.doi | 10.6342/NTU202103685 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-10-15 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
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