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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90806完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張智星 | zh_TW |
| dc.contributor.advisor | Jyh-Shing Jang | en |
| dc.contributor.author | 陳欣惠 | zh_TW |
| dc.contributor.author | Xin-Hui Tan | en |
| dc.date.accessioned | 2023-10-03T17:42:21Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-10-03 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-08 | - |
| dc.identifier.citation | M. M. Ahsan, S. A. Luna, and Z. Siddique. Machine-learning-based disease diagnosis: A comprehensive review. Healthcare, 10(3):541, Mar 2022.
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A machine learning model for pre-dialysis prediction of intradialytic hypotension risk. Acta Nephrologica, 36(4):pp. 212–221, 2022. 林冠宇. 光學線上監測系統用於血液透析與資料分析. 碩士論文, 國立臺灣大學生物機電工程學系, 2021. 臺灣博碩士論文知識加值系統. https: //hdl.handle.net/11296/jmm6w3. 衛生福利部中央健康保險署 (2023 年 3 月 21 日更新). 國人全民健康保險就醫疾病資訊. 查詢日期:2023 年 4 月 6 日，檢自:https://www.nhi.gov.tw/ Content_List.aspx?n=D529CAC4D8F8E77B&topn=23C660CAACAA159D. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90806 | - |
| dc.description.abstract | 慢性腎臟病是台灣重要的醫療課題，約九成的慢性腎臟病末期患者會採取腎臟替代療法中的血液透析進行治療。而透析中低血壓(intradialytic hypotension, IDH)是血液透析患者常見的併發症，有研究表明 IDH 會增加患者心血管疾病發病率和死亡率。本研究合理運用台大醫院血液透析患者的透析數據，採用較為嚴格之 IDH 定義(考量患者發生的症狀以及醫護人員的干預和處置)對資料集進行標注，有別於其他研究工作僅以透析中的血壓變化來定義 IDH。基於台大醫院的透析資料集，設計若干實驗以探討機器學習在即時偵測和預測未來有症狀之 IDH 的可行性。實驗結果顯示，XGBoost 和 RF 演算法在即時偵測有症狀之 IDH 的靈敏度和特異度皆可達 0.8;LSTM 和 MLP 在預測未來有症狀之 IDH 時，擴增輸入的時間序列資料，模型預測的能力得以提升。 | zh_TW |
| dc.description.abstract | Chronic Kidney Disease (CKD) is an important medical issue in Taiwan. Approximately 90% of end-stage renal disease (ESRD) patients are treated with hemodialysis. Intradialytic hypotension (IDH) is a common complication among hemodialysis patients. Studies have shown that IDH increases the incidence of cardiovascular diseases and mortality. Considering the intradialytic symptoms and the intradialytic interventions, with the hemodialysis data from National Taiwan University Hospital, this thesis aims to employ a rigorous definition of IDH to define IDH during dialysis treatment, unlike other studies that solely relies on blood pressure drops. Our experiments are designed to explore the feasibility of Machine Learning in detection and prediction of IDH with symptoms. The experimental results demonstrate that XGBoost and Random Forest algorithms achieve a sensitivity and specificity of 0.8 in the detection of IDH with symptoms. When we provide more time series data, the performance of LSTM and MLP models for predicting IDH with symptoms are improved. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-10-03T17:42:21Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-10-03T17:42:21Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 致謝 i
摘要 iii Abstract v 目錄 vii 圖目錄 xi 表目錄 xiii 第一章 緒論 1 1.1 背景.................................... 1 1.2 研究動機................................. 2 1.3 章節概述................................. 3 第二章 文獻探討 5 2.1 人工智慧、機器學習、深度學習之關聯................ 5 2.2 機器學習................................. 5 2.2.1 監督式學習.............................. 6 2.2.2 非監督式學習 ............................ 6 2.2.3 半監督式學習 ............................ 7 2.2.4 強化學習............................... 8 2.3 深度學習................................. 8 2.4 透析中低血壓的定義 .......................... 9 2.5 ML預測IDH的相關文獻........................ 10 第三章 資料集和任務介紹 13 3.1資料集介紹................................ 13 3.1.1 檔案說明............................... 13 3.1.2 患者分佈............................... 15 3.2 清理真實值................................ 15 3.2.1 定義有症狀之IDH.......................... 16 3.2.2 有症狀之IDH比例 ......................... 17 3.3 任務介紹................................. 18 3.3.1 即時偵測............................... 19 3.3.2 未來預測............................... 20 3.3.3 評估方式............................... 21 3.3.3.1 混淆矩陣、靈敏度、特異度.............. 21 3.3.3.2 懲罰矩陣 ........................ 22 3.3.3.3 正規化懲罰....................... 23 第四章 研究方法 25 4.1資料前處理................................ 25 4.1.1 異常值 ................................ 25 4.1.2 特徵選擇............................... 26 4.1.3 空值處理............................... 27 4.1.4 特徵工程............................... 29 4.1.5 特徵轉換............................... 29 4.2 資料集切割................................ 31 4.3 不平衡資料集 .............................. 32 4.4 機器學習演算法............................. 33 4.4.1 極限梯度提升 ............................ 33 4.4.2 輕量化梯度提升機.......................... 34 4.4.3 支持向量機.............................. 35 4.4.4 隨機森林............................... 37 4.5 深度學習模型架構............................ 38 4.5.1 長短期記憶.............................. 38 4.5.2 堆疊式長短期記憶.......................... 39 4.5.3 卷積神經網路-長短期記憶 ..................... 39 4.5.4 卷積長短期記憶 ........................... 40 4.5.5 多層感知器.............................. 41 4.6 損失函數................................. 42 4.6.1 交叉熵 ................................ 42 4.6.2 二元交叉熵.............................. 42 4.7 優化算法................................. 43 4.7.1 SGD.................................. 43 4.7.2 Adagrad................................ 44 4.7.3 RMSProp ............................... 44 4.7.4 Adam ................................. 45 第五章 實驗設計和結果討論 47 5.1 實驗流程與設定............................. 47 5.2 即時偵測................................. 49 5.2.1 實驗一 ................................ 50 5.2.2 實驗二 ................................ 53 5.3 未來預測................................. 54 5.3.1 實驗三 ................................ 55 5.3.2 實驗四 ................................ 57 第六章 結論和未來工作 61 6.1 結論.................................... 61 6.2 未來工作................................. 62 參考文獻 63 附錄 A — 實驗結果數據 69 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 時間序列分類 | zh_TW |
| dc.subject | 類別不平衡 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 透析中低血壓 | zh_TW |
| dc.subject | 慢性腎臟病 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | Chronic Kidney Disease | en |
| dc.subject | Deep Learning | en |
| dc.subject | Class Imbalance | en |
| dc.subject | Machine Learning | en |
| dc.subject | Intradialytic Hypotension | en |
| dc.subject | Time Series Classification | en |
| dc.title | 運用機器學習進行透析中低血壓之偵測 | zh_TW |
| dc.title | Detecting Intradialytic Hypotension Using Machine Learning | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 盧彥文;周鈺翔 | zh_TW |
| dc.contributor.oralexamcommittee | Yen-Wen Lu;Yu-Hsiang Chou | en |
| dc.subject.keyword | 透析中低血壓,慢性腎臟病,機器學習,深度學習,類別不平衡,時間序列分類, | zh_TW |
| dc.subject.keyword | Intradialytic Hypotension,Chronic Kidney Disease,Machine Learning,Deep Learning,Class Imbalance,Time Series Classification, | en |
| dc.relation.page | 71 | - |
| dc.identifier.doi | 10.6342/NTU202302763 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2023-08-10 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資訊工程學系 | - |
| 顯示於系所單位： | 資訊工程學系 | |
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