Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/81165
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dc.contributor朱彥煒zh_TW
dc.contributor.author劉緯陽zh_TW
dc.contributor.authorLiu, Wayne-Youngen_US
dc.contributor.other生命科學院碩士在職專班zh_TW
dc.date2013en_US
dc.date.accessioned2014-06-13T08:11:07Z-
dc.date.available2014-06-13T08:11:07Z-
dc.identifierU0005-1108201310165500en_US
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ACM SIGKDD Explorations Newsletter 2009, 11(1):10-18. 40. Ito H, Kawahara T, Terao H, Ogawa T, Yao M, Kubota Y, Matsuzaki J: The most reliable preoperative assessment of renal stone burden as a predictor of stone-free status after flexible ureteroscopy with holmium laser lithotripsy: a single-center experience. Urology 2012, 80:524-528.en_US
dc.identifier.urihttp://hdl.handle.net/11455/81165-
dc.description.abstract根據統計數據顯示,在中段或下段輸尿管位置的結石以內視鏡排除的成功率相較於使用體外震波碎石術排石的成功率高20%與19%,而在上段輸尿管結石兩種治療的成功率僅差距1%,較難成功區分。若能透過輔助系統預測術後的成功率,將可避免無效的醫療進而減少不必要的醫療成本支出。本研究收集417位上段輸尿管結石且接受體外震波碎石術排石之患者資料,其中包含一次碎石與二次碎石術後成功與失敗之病例。利用患者個體與結石特徵共16項資訊建構一次碎石與二次碎石術後結果預測系統。而術後結果除了與病例特徵有關外,體外震波碎石機的設定也有其影響性,因此以該資料延伸出預測可能使術後成功之體外震波碎石機kV建議值。在特徵的分析上,患者的多次碎石經驗與體外震波病史資訊,對於在一次與二次碎石及kV值之選用差異與重要性。並透過特徵選擇重新驗證結石長度之重要性,也找到新的特徵結石面積用以推測術後成功率相較傳統以結石長度更高的準確,做為臨床診斷上的新參考。目前尚未有針對二次碎石進行討論之研究,而本論文最終以RandomTree克服二次碎石資料少的問題,成功發展出準確度ROC達 0.76的預測模型。配合透過37種方法挑選出準確度ROC 達0.80的一次碎石成功率預測模型,三種涉及體外震波碎石療程之工具建構本系統,提供輔臨床中設計有效的治療計劃,進而改善碎石品質。zh_TW
dc.description.tableofcontents目錄 摘要 i Abstract ii 目錄 iii 圖目錄 iv 表目錄 v 1 緒論 1 1.1 研究背景與動機 1 1.2 研究目的與貢獻 2 2 相關研究 3 2.1 交叉驗證 3 2.2 評估方法 4 3 材料與方法 6 3.1 資料集 6 3.2 特徵編碼 7 3.3 實驗流程 9 3.4 系統流程 13 4 結果 14 4.1 kV預測結果 14 4.2 一次ESWL預測結果 17 4.2.1 門檻值 17 4.2.2 機器學習 19 4.3 二次ESWL預測結果 22 4.4 系統展示 25 5 討論 27 5.1 kV決策規則 27 5.2 一次ESWL特徵分析 31 5.3 二次ESWL關聯規則 34 5.4 二次ESWL決策規則 36 5.5 二次ESWL特徵分析 37 5.6 其他研究比較 39 6 結論 40 7 參考文獻 42 8 補充材料 46zh_TW
dc.language.isozh_TWen_US
dc.publisher生命科學院碩士在職專班zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1108201310165500en_US
dc.subject體外震波碎石術zh_TW
dc.subjectExtracorporeal shock wave lithotripsy(ESWL)en_US
dc.subject上段輸尿管zh_TW
dc.subject結石zh_TW
dc.subject仟伏特zh_TW
dc.subject機器學習zh_TW
dc.subject一次體外震波碎石zh_TW
dc.subject二次體外震波碎石zh_TW
dc.subjectUpper ureteral stoneen_US
dc.subjectkVen_US
dc.subjectMachine learningen_US
dc.subjectSingle ESWLen_US
dc.subjectSecond ESWLen_US
dc.title運用機器學習方法預測上段輸尿管結石患者接受體外震波碎石治療後的結果zh_TW
dc.titleApplying Machine Learning Approach to Predict Outcomes of Patients with Upper Ureteral Stones after Extracorporeal Shock Wave Lithotripsyen_US
dc.typeThesis and Dissertationzh_TW
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeThesis and Dissertation-
item.cerifentitytypePublications-
item.fulltextno fulltext-
item.languageiso639-1zh_TW-
item.grantfulltextnone-
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