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研究生:葉時昊
研究生(外文):Shih-Hao Yeh
論文名稱:應用openEHR臨床指引定義語言建立急性腎損傷風險推論引擎
論文名稱(外文):Applying the openEHR's Guideline Definition Language to build Acute Kidney Injury Risk Inference Engine
指導教授:劉德明劉德明引用關係
指導教授(外文):Der-Ming Liou
學位類別:碩士
校院名稱:國立陽明大學
系所名稱:生物醫學資訊研究所
學門:生命科學學門
學類:生物化學學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:中文
論文頁數:50
中文關鍵詞:急性腎損傷openEHR標準Drools推論引擎顯影劑
外文關鍵詞:AKIopenEHRDroolsContrast Medium
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研究背景:急性腎損傷(AKI)是住院患者的常見並發症,是住院死亡的重要原因。每年全球約有二百萬人死於AKI。AKI不僅是一個醫療問題,而且已經成為一個重大的公共衛生問題。臨床決策支援系統可以依據電腦化的臨床指引計算患者發病的機率,並給予醫生決策的建議,推測發病的風險,有助於確保患者的安全。
目標:openEHR指引定義語言是用來描述臨床指引的語言。本研究目的為應用openEHR指引定義語言,建立一個急性腎損傷的風險推論引擎。此推論引擎可以與醫院的病歷資料連接,使決策支援系統與電子病歷更緊密的結合。
研究方法:本研究的研究內容分成兩大部分,其一是醫學知識模型與電腦化臨床指引的建立,以醫生的專業知識作為模型設計的依據;並以指引定義語言將急性腎損傷的臨床指引轉換成電腦可以判讀的規則;其二為建立可以將病人數據匯入推論引擎中的解析器,達到臨床決策支援系統與電子健康紀錄結合的目的。
研究結果:本研究的系統符合openEHR規範,其擴充性與可操作性優於將參數撰寫於原始碼中的臨床決策支援系統。本論文以急性腎損傷為例子。透過解析器與電子病歷連接後,使醫生可以簡單且快速的調整決策支援系統中的規則,並解決臨床上遇到的問題,方便醫學診斷的研究。
Background: Acute kidney injury (AKI) is a common complication among hospitalized patients, and is an important cause for in-hospital death. Approximately two million people die from AKI per year around the word. AKI has become not only a medical problem, but also a major public health concern. By utilizing the Clinical Decision Support System (CDSS) are capable of predicting the risk of illness, and improving patients’ safety.
Aim: The openEHR Guideline Definition Language (GDL) is a formal language to describe the logic of decision support. The objective of this study is to establish an AKI risk inference engine based on openEHR GDL, and create the connection between Electronic Health Record (EHR) and CDSS tighter than previously.
Methods: This research is divided into two major sections as follows. First, the medical knowledge model is established by Archetype Definition Language (ADL), and Computer-Interpretable Guidelines (CIGs) is established by GDL. Second, in order to integrate CDSS with EHR more effectively, we build a parser able to import patients’ data to the inference engine.
Results: The system is in compliance with openEHR standard specification in this research. The scalability, operability and maintainability of this system are superior to the traditional hardcoding domain knowledge system. Besides, the clinical decision rules can be adjusted quickly and simply. Therefore, physicians are able to reduce the cost of clinical problem-solving process with the help of Acute Kidney Injury Risk Inference Engine.
誌謝 i
中文摘要 ii
Abstract iii
目錄 v
圖目錄 vi
表目錄 viii
縮寫表 ix
中英文對照表 x
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機 4
第三節 研究目標 8
第四節 論文架構 9
第二章 文獻探討 10
第一節 急性腎損傷臨床診斷指引 10
第二節 電腦化臨床指引語言 12
第三節 Drools推論引擎 19
第三章 系統設計 21
第一節 開發環境 21
第二節 系統架構 22
第三節 系統使用情境 30
第四章 系統實作與評估 32
第一節 系統實作 32
第二節 系統結果與評估 37
第五章 討論與結論 41
第一節 臨床決策支援系統的比較 41
第二節 系統應用限制 42
第三節 未來展望 43
第四節 結論 44
參考資料 46
附錄 50
附錄一、程式原始碼 50

圖目錄
圖 1-1 美國腎病協會 2015 年統計報告[5]..3
圖 1-2 openEHR 雙層建模概念圖 ..........8
圖 2-1 急性腎損傷的風險因子分數表 ......11
圖 2-2 PROforma 四種任務類型............14
圖 2-3 乳腺癌指引的任務模型[25].........15
圖 2-4 乳腺癌決策指引示意圖[25].........16
圖 2-5 SEGA 社區型肺炎臨床指引的決策流程圖[28]......18
圖 2-6 推論引擎架構 ................................20
圖 3-1 臨床決策支援系統架構圖 ......................22
圖 3-2 openEHR 原型的分類 ..........................23
圖 3-3 醫學知識模型資料庫[32].......................24
圖 3-4 openEHR 提供的醫學知識模型以血壓為例[32].....26
圖 3-5 指引定義語言的定義描述 ......................28
圖 3-6 指引定義語言與原型定義語言的關聯定義 ........29
圖 3-7 指引定義語言判斷的規則庫 ....................29
圖 3-8 指引定義語言與醫學術語的連結描述 ............29
圖 3-9 系統導入醫院使用流程 ........................30
圖 4-1 急性腎損傷風險分數表的原型 ..................32
圖 4-2 問題決策判斷的原型 ..........................33
圖 4-3 急性腎損傷的臨床決策指引[20].................34
圖 4-4 指引定義語言引用的原型 ......................35
圖 4-5 指引定義語言編輯器規則範例 ..................36
圖 4-6 醫學術語的連結 ..............................37
圖 4-7 根據病歷號匯入院內病患資料 ..................38
圖 4-8 病患資料帶入引擎後推論結果 ..................38

表目錄
表 2-1 急性腎損傷風險因子對應 ICD10 健保診斷代碼表 ......12
表 3-1 急性腎損傷風險因子表 .............................25
表 5-1 決策支援系統比較表 ...............................42
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