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研究生:黃煜庭
研究生(外文):Yu-Ting Huang
論文名稱:利⽤機器學習並根據臨床檢驗數據及⾝體參數以預測阻塞性睡眠呼吸中⽌症之⾵險
論文名稱(外文):Using Machine Learning to Predict the Risk of Obstructive Sleep Apnea Based on Laboratory Data and Body Profile
指導教授:蘇家玉蘇家玉引用關係
指導教授(外文):Emily Chia-Yu Su
口試委員:劉文德邱泓文蘇家玉
口試委員(外文):Wen-Te LiuHung-Wen ChiuEmily Chia-Yu Su
口試日期:2023-06-26
學位類別:碩士
校院名稱:臺北醫學大學
系所名稱:醫學院人工智慧醫療碩士在職專班
學門:醫藥衛生學門
學類:醫學技術及檢驗學類
論文種類:學術論文
論文出版年:2023
畢業學年度:111
語文別:中文
論文頁數:91
中文關鍵詞:睡眠中心醫學檢驗科機器學習阻塞性睡眠呼吸中止症身體參數臨床檢驗數據
外文關鍵詞:Sleep CenterDepartment of Laboratory MedicineMachine LearningObstructive Sleep ApneaBody ProfileLaboratory Data
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背景:阻塞性睡眠呼吸中止症為反覆性的上呼吸道塌陷或阻塞,導致呼吸過程費力且氣流變淺,而嚴重者可能造成窒息。協助提早發現阻塞性睡眠呼吸中止症為重要的目標。
目的:分析臨床檢驗數據與阻塞性睡眠呼吸中止症相關性,並建構預測阻塞性睡眠呼吸中止症風險之機器學習模型。
方法:資料來源取自衛生福利部雙和醫院(委託臺北醫學大學興建經營)之睡眠中心與醫學檢驗科。收案日期自2016年至2021年,包含20歲至90歲成人與年長者,使用SPSS軟體進行統計資料分析,使用機器學習建構預測模型,提供給有需求的人(如:健檢客戶)一個可以預測阻塞性睡眠呼吸中止症風險的工具。
結果:針對AHI 15 without MiniO2預測模型,AUC,(95% CI)達到0.848(0.836 - 0.860);對於AHI 15 with MiniO2預測模型,AUC,(95% CI)達到0.888(0.882 - 0.894)。在AHI 30 without MiniO2預測模型中,AUC,(95% CI)達到0.809(0.797 - 0.821);而在AHI 30 with MiniO2預測模型中,AUC,(95% CI)達到0.857(0.851 - 0.863)。身體參數結合臨床檢驗數據以及最低血氧飽和度可有效預測阻塞性睡眠呼吸中止症之風險。
Background:Obstructive sleep apnea is recurrent upper airway collapse or obstruction, it causes breathing difficulty and reduced airflow, which may cause suffocation in severe cases. Early detection of obstructive sleep apnea is an important research aim.
Purpose:Analyze the correlation between laboratory data and obstructive sleep apnea, and construct a machine learning model to predict the risk of obstructive sleep apnea.
Methods:The source of data is taken from the Sleep Center and Department of Laboratory Medicine of Taipei Medical University Shuang Ho Hospital (New Taipei City, Taiwan)between January 2016 and December 2021, This study included patients who were aged between 20 and 90 years, Use SPSS software for statistical data analysis, Constructing a predictive model using machine learning, we have created a predictive model that provides a tool for individuals with a need, such as health check-up clients, to predict their risk of obstructive sleep apnea.
Results:In this study, for the AHI 15 without MiniO2 prediction model, the AUC(95% CI) was 0.848(0.836 - 0.860). For the AHI 15 with MiniO2 prediction model, the AUC(95% CI) was 0.888(0.882 - 0.894). In the AHI 30 without MiniO2 prediction model, the AUC(95% CI) was 0.809(0.797 - 0.821). In the AHI 30 with MiniO2 prediction model, the AUC(95% CI) was 0.857(0.851 - 0.863). Combining Body profile with laboratory data and Minimum peripheral oxygen saturation can effectively predict the risk of obstructive sleep apnea.
誌謝 i
目錄 ii
表目錄 v
圖目錄 vii
摘要 viii
Abstract ix
第一章 緒論 1
第二章 文獻探討 3
2-1 阻塞性睡眠呼吸中止症介紹 3
2-2 阻塞性睡眠呼吸中止症相關危險因子 3
2-3 阻塞性睡眠呼吸中止症之診斷工具與方法 6
2-4 阻塞性睡眠呼吸中止症之機器學習預測 7
2-5 研究方向與目標 8
第三章 研究方法與步驟 12
3-1 阻塞性睡眠呼吸中止症病患之資料來源 12
3-1-1 資料來源 12
3-1-2 研究對象 13
3-1-3 研究資料範圍 14
3-2 阻塞性睡眠呼吸中止症病患之研究方法 14
3-3 阻塞性睡眠呼吸中止症病患之研究步驟 15
3-3-1 資料前處理 16
3-3-2 特徵值類別 23
3-3-3 目標資料定義 23
3-3-4 研究工具統計檢定方法 26
3-3-5 機器學習演算法 26
3-3-6 相關參數設定 28
第四章 分析與結果 29
4-1 T檢定和卡方檢定結果表現 29
4-2 模型選取 35
4-3 頸圍特徵值排除之模型表現 36
4-4 特徵選取 37
4-5 特徵選取後之5-Fold Cross-Validation結果展示 39
4.6 加入最低血氧飽和度之模型表現 40
4-7 最終模型結果展示 40
4-7-1 最終模型結果展示(5-Fold Cross-Validation) 40
4-7-2 最終模型結果展示(AHI 15 Testing Dataset Features without MiniO2) 42
4-7-3 最終模型結果展示(AHI 15 Testing Dataset Features with MiniO2) 43
4-7-4 最終模型結果展示(AHI 30 Testing Dataset Features without MiniO2) 44
4-7-5 最終模型結果展示(AHI 30 Testing Dataset Features with MiniO2) 45
第五章 討論 47
5-1 統計分析的結果與評估 47
5-2 使用身體參數建構模型的比較 47
5-3 特徵篩選的方法與選擇 48
5-4 BMI與頸圍特徵的排除與否 48
5-5 中度至重度阻塞性睡眠呼吸中止症族群對模型的影響 49
5-6 不同性別與年齡層在模型下的表現 49
5-7 最終結果的解釋和評估 50
5-8 模型的實用性和意義 50
5-9 研究的局限性與結果的限制 51
第六章 結論與未來展望 52
參考資料 53
英文文獻 53
中文文獻 58
英文電子資料 59
中文電子資料 59
附錄一:JIRB-N202212067 60
附錄二:AHI 15與AHI 30的統計描述 61
附錄三:AHI 15决策樹圖片展示 62
附錄四:AHI 30决策樹圖片展示 64
附錄五:AHI 15及AHI 30特徵選取後之模型測試結果 66
附錄六:SHAP Value AHI 15 with or without MiniO2 67
附錄七:SHAP Value AHI 30 with or without MiniO2 69
附錄八:比較排除GPT與否的AUC表現 71
附錄九:模型預測結果的比較 72
附錄十:全國法規資料庫 勞工健康保護規則 第三章 健康檢查及管理 第17條 73
附錄十一:衛生福利部國民健康署-成人預防保健 74
附錄十二:臨床檢驗數據之時間差對模型的影響 75
附錄十三:區分性別和年齡與否的訓練模型比較 76
附錄十四:Python程式碼展示 84
附錄十五:身體參數建構模型比較 89
附錄十六:預測模型間的結果比較 89
附錄十七:大於五十歲女性之統計分析結果 90
附錄十八:AHI 15模型的最終結果是否達到當初設定目標 90
附錄十九:AHI 30模型的最終結果是否達到當初設定目標 91
表目錄
表1 阻塞性睡眠呼吸中止症相關危險因子(臨床檢驗數據) 5
表2 透過影像預測阻塞性睡眠呼吸中止症之人工智慧相關文獻 10
表3 透過數據預測阻塞性睡眠呼吸中止症之人工智慧相關文獻 11
表4 研究資料範圍 14
表5 排除含有缺失值、異常值及特定族群的受測者 17
表6 訓練組最終人數統計(AHI 15 Training Dataset) 18
表7 訓練組最終人數統計(AHI 30 Training Dataset) 18
表8 測試組最終人數統計(AHI 15 Testing Dataset) 18
表9 測試組最終人數統計(AHI 30 Testing Dataset) 18
表10 AHI15 Training Dataset臨床檢驗數據項目保留與缺失值的補值 19
表11 AHI30 Training Dataset臨床檢驗數據項目保留與缺失值的補值 20
表12 AHI15 Testing Dataset臨床檢驗數據項目保留與缺失值的補值 21
表13 AHI30 Testing Dataset臨床檢驗數據項目保留與缺失值的補值 22
表14 機器學習模型與參數調整 27
表15 AHI15 Training Dataset統計分析 31
表16 AHI30 Training Dataset統計分析 32
表17 AHI15 Testing Dataset統計分析 33
表18 AHI30 Testing Dataset統計分析 34
表19 不同演算法進行訓練,5-Fold Cross-Validation結果展示 35
表20 不同演算法進行訓練,Testing Dataset結果展示 36
表21 評估排除頸圍後的AUC表現,5-Fold Cross-Validation結果展示 36
表22 特徵篩選後的AUC表現,5-Fold Cross-Validation結果展示 39
表23 特徵包含最低血氧飽和度的AUC表現,5-Fold Cross-Validation結果展示 40
表24 最終模型結果展示(5-Fold Cross-Validation) 41
表25 最終模型結果展示(AHI 15 Testing Dataset Features without MiniO2) 42
表26 最終模型結果展示(AHI 15 Testing Dataset Features with MiniO2) 43
表27 最終模型結果展示(AHI 30 Testing Dataset Features without MiniO2) 44
表28 最終模型結果展示(AHI 30 Testing Dataset Features with MiniO2) 46
圖目錄
圖 1 資料收集 13
圖 2 阻塞性睡眠呼吸中止症之研究架構圖 15
圖 3 資料前處理流程圖 16
圖 4 實驗分析流程圖 30
圖 5 Feature Selection(AHI 15 Features without MiniO2) 37
圖 6 Feature Selection(AHI 30 Features without MiniO2) 38
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