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藥物不良反應成因的分析與偵測之知識發掘平台 = A Knowledge ...

國立高雄大學資訊工程學系碩士班

藥物不良反應成因的分析與偵測之知識發掘平台 = A Knowledge Discovery Platform for Analyzing and Detecting Adverse Drug Reactions

紀錄類型:	書目-語言資料,印刷品 : 單行本
並列題名:	A Knowledge Discovery Platform for Analyzing and Detecting Adverse Drug Reactions
作者:	李和益,
其他團體作者:	國立高雄大學
出版地:	[高雄市]
出版者:	撰者;
出版年:	2009[民98]
面頁冊數:	97面圖、表 : 30公分;
標題:	分類
標題:	Adverse drug reaction
附註:	參考書目：面
附註:	指導教授：林文揚
摘要註:	藥物不良反應是用藥安全議題中一個重要且不容忽視的問題，特別是許多的不良反應無法在藥物上市前藉由有限的臨床試驗發掘，往往須等上市後，經由大量的使用方能突顯出來，故如何偵測、及早發掘藥物引起的不良反應在醫藥界為重要的研究議題。近年來，隨著大量的不良反應事件被紀錄以及資料探勘方法的蓬勃發展，逐漸有學者或機構以統計或資料探勘發展藥物不良反應的偵測方法。然而這些方法普遍缺乏與知識發掘系統的整合，在分析或探勘的過程中，使用者都必須長時間的等待，無法提供給使用者一個交談式的環境。有鑑於此，我們開發一個交談式的系統環境，結合藥物不良反應的資料倉儲，一方面可以利用線上分析處理(OLAP)工具，讓使用者可以從不同角度去觀察分析，另一方面利用資料探勘的方法，從資料倉儲裡挖掘出藥物與症狀的關聯，並加入了使用者感興趣的其他因素，像是病人資料。對於藥物與症狀的關聯規則，我們考慮了四種規則形式，包括單一藥物導致單一症狀，藥物交互作用導致單一症狀，單一藥物導致併發症，以及藥物交互作用導致併發症。我們提出了兩個演算法，針對前兩種規則形式，實驗證明的確可以有效率的探勘出對應的關聯規則。第三種規則和第四種規則形式目前則是提出我們的演算法概念，未來將會完成其相關研究工作。 Adverse Drug Reaction (ADR) is one of the most important issues on drug safety assessment and should not be ignored. In fact, many adverse drug reactions cannot be discovered through limited pre-marketing clinical trials. Instead, they can only be recognized by a long term of post-marketing surveillance of drug usages. In light of this, how to detect adverse drug reactions as early as possible is thus an important research topic in the pharmaceutical industry. Recently, the accumulation of large volumes of adverse events and the flourish of data mining technologies have encouraged the development of statistical and data mining methods for detecting ADRs. These stand-alone methods, without integration into knowledge discovery systems, are tedious and inconvenient to users and the processes of exploration are time-consuming. In this thesis, we thus propose an interactive system platform for ADRs detection. By integrating the concept of ADRs data warehouse and innovative frequent pattern mining techniques, the proposed system can not only support OLAP style of multidimensional analysis of ADRs, but also offer interactive discovery of associations between drugs and symptoms, called drug-ADR association rule, which can be further specialized by other factors interesting to users, such as demographic information. We consider four types of drug-ADR association rules, including associations of single drug and single symptom, multiple drugs (interactions) and single symptom, single drug and multiple symptoms, and multiple drugs and multiple symptoms. We propose two efficient algorithms to accomplish interactive discovery of the first two types of association patterns. Experiments indicate that interesting and valuable drug-ADR association rules can be efficiently mined. As to the third and the fourth types, we propose the concepts of mining methods; their works will complete in the future.

藥物不良反應成因的分析與偵測之知識發掘平台 = A Knowledge Discovery Platform for Analyzing and Detecting Adverse Drug Reactions
李, 和益

藥物不良反應成因的分析與偵測之知識發掘平台 = A Knowledge Discovery Platform for Analyzing and Detecting Adverse Drug Reactions / 李和益撰 - [高雄市] : 撰者, 2009[民98]. - 97面 ; 圖、表 ; 30公分.
參考書目：面指導教授：林文揚.
分類Adverse drug reaction

藥物不良反應成因的分析與偵測之知識發掘平台 = A Knowledge Discovery Platform for Analyzing and Detecting Adverse Drug Reactions
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330 $a 藥物不良反應是用藥安全議題中一個重要且不容忽視的問題，特別是許多的不良反應無法在藥物上市前藉由有限的臨床試驗發掘，往往須等上市後，經由大量的使用方能突顯出來，故如何偵測、及早發掘藥物引起的不良反應在醫藥界為重要的研究議題。近年來，隨著大量的不良反應事件被紀錄以及資料探勘方法的蓬勃發展，逐漸有學者或機構以統計或資料探勘發展藥物不良反應的偵測方法。然而這些方法普遍缺乏與知識發掘系統的整合，在分析或探勘的過程中，使用者都必須長時間的等待，無法提供給使用者一個交談式的環境。有鑑於此，我們開發一個交談式的系統環境，結合藥物不良反應的資料倉儲，一方面可以利用線上分析處理(OLAP)工具，讓使用者可以從不同角度去觀察分析，另一方面利用資料探勘的方法，從資料倉儲裡挖掘出藥物與症狀的關聯，並加入了使用者感興趣的其他因素，像是病人資料。對於藥物與症狀的關聯規則，我們考慮了四種規則形式，包括單一藥物導致單一症狀，藥物交互作用導致單一症狀，單一藥物導致併發症，以及藥物交互作用導致併發症。我們提出了兩個演算法，針對前兩種規則形式，實驗證明的確可以有效率的探勘出對應的關聯規則。第三種規則和第四種規則形式目前則是提出我們的演算法概念，未來將會完成其相關研究工作。 Adverse Drug Reaction (ADR) is one of the most important issues on drug safety assessment and should not be ignored. In fact, many adverse drug reactions cannot be discovered through limited pre-marketing clinical trials. Instead, they can only be recognized by a long term of post-marketing surveillance of drug usages. In light of this, how to detect adverse drug reactions as early as possible is thus an important research topic in the pharmaceutical industry. Recently, the accumulation of large volumes of adverse events and the flourish of data mining technologies have encouraged the development of statistical and data mining methods for detecting ADRs. These stand-alone methods, without integration into knowledge discovery systems, are tedious and inconvenient to users and the processes of exploration are time-consuming. In this thesis, we thus propose an interactive system platform for ADRs detection. By integrating the concept of ADRs data warehouse and innovative frequent pattern mining techniques, the proposed system can not only support OLAP style of multidimensional analysis of ADRs, but also offer interactive discovery of associations between drugs and symptoms, called drug-ADR association rule, which can be further specialized by other factors interesting to users, such as demographic information. We consider four types of drug-ADR association rules, including associations of single drug and single symptom, multiple drugs (interactions) and single symptom, single drug and multiple symptoms, and multiple drugs and multiple symptoms. We propose two efficient algorithms to accomplish interactive discovery of the first two types of association patterns. Experiments indicate that interesting and valuable drug-ADR association rules can be efficiently mined. As to the third and the fourth types, we propose the concepts of mining methods; their works will complete in the future.
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