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研究生:吳家丞
研究生(外文):Wu, Chia-Cheng
論文名稱:機器學習在診斷假體周圍感染中的應用
論文名稱(外文):Application of Machine Learning to Diagnosis of Periprosthetic Joint Infection: Comparison between Ensemble Meta Machine Learning and 2018 International Consensus Meeting Scoring System
指導教授:胡毓志
指導教授(外文):Hu, Yuh-Jyh
口試委員:楊武黃崇源
口試委員(外文):Yang, WuuHuang, Chung-Yuan
口試日期:2021-01-12
學位類別:碩士
校院名稱:國立交通大學
系所名稱:數據科學與工程研究所
學門:電算機學門
學類:軟體發展學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:中文
論文頁數:37
中文關鍵詞:機器學習資料探勘
外文關鍵詞:Explainable AIStacking
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  • 被引用被引用:0
  • 點閱點閱:190
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  • 收藏至我的研究室書目清單書目收藏:0
機器學習已廣泛應用於醫學中,儘管用於診斷假體周圍關節感染(PJI)的文獻很少。本研究評估了機器學習在PJI診斷中的可行性,並將其預測性能與臨床醫生通常使用的評分標準進行比較。我們將PJI診斷視為一種歸納性學習問題來解決,其中每筆患者記錄都由一組患者特徵表示。在模型的選擇上,相較於從一系列患者記錄中學習單個診斷模型,我們更傾向將多個預測模型結合起來,以利用多個預測變量的協同作用來實現卓越的性能。最終,我們創建了一個兩層堆疊學習器,其中包括四個基本分類器和一個元分類器。為了解決機器學習的可理解性問題,我們開發了基於代理人的解釋生成器,以提供可理解的解釋。為了評估元學習者的表現,我們對323名亞洲患者進行了交叉驗證,以進行比較。我們為PJI預測開發了一個集成的元學習器。與目前骨科醫學學會所訂定的評分標準相比,機器學習在PJI診斷中是適用的且具有競爭力的。
Machine learning has been widely applied in medicine, although little has been published regarding periprosthetic joint infection (PJI) diagnosis. Here, we evaluated machine learning feasibility for PJI diagnosis and compared its predictive performance with that of scoring criteria commonly used by clinicians. We addressed PJI diagnosis as an inductive learning problem, wherein each patient record is represented by a set of patient features. Rather than learning a single diagnosis model from a series of patient records, we propose combining multiple prediction models to achieve superior performance by utilizing the synergy of multiple predictors. We created a two-level meta stacking learner comprising four base classifiers and one meta-classifier. To resolve the comprehensibility issue of machine learning, we developed a surrogate-based explanation generator to provide understandable interpretations. To assess the meta learner’s performance, we cross-validated 323 Asian patients for comparison.We developed an ensemble meta learner for PJI prediction. It is suggested that machine learning is applicable and competitive for PJI diagnosis as compared with the currently widely accepted scoring criteria.
第一章、 緒論 1
1.1 研究背景與動機 1
1.2 研究目標 2
第二章、 相關方法與文獻 3
2.1 演算法 3
2.1.1 隨機森林 (Random Forest, RF) 3
2.1.2 羅吉斯回歸 (Logistic Regression, LR) 4
2.1.3  貝氏分類器 (Naïve Bayes, NB) 5
2.1.4 XGBoost (Extreme Gradient Boosting, XGB) 5
2.1.5 支持向量機 (Support Vector Machine, SVM) 7
2.1.6 Stacking 8
第三章、 實驗方法 9
3.1 研究對象與預測比較 9
3.2 效能評估 12
3.3 以PJI-AR預測作為歸納學習 14
3.4 集成元學習--堆疊泛化 15
3.5 可解釋的集成學習 17
3.6 PJI預測與解釋 ━ PJI-PI 20
第四章、 實驗結果 21
4.1 PJI預測的ML和ICM標準比較 21
4.2 PJI-PI模型解釋生成器 23
4.3 重要特徵 27
第五章、 討論 29
第六章、 結論 31
第七章、 參考文獻 32
附錄 36
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