藥物不良反應已經成為患病和死亡的主要原因之一，並對健康照護造成龐大耗損。目前已有許多方法使用在藥物安全監測，如自發性通報系統資料庫和電子健康紀錄資料庫。自發性通報系統資料庫具有資訊不完全、通報不足等問題，可能導致偏差的結果，而據信電子健康紀錄資料庫能夠輔助既有的自發性通報系統資料庫。因此，本研究致力於發展一種新式的分析架構及流程，整合各種藥物不良反應訊號偵測方法，以發現電子健康紀錄資料庫中潛在的藥物副作用。根據藥物和藥物不良反應的出現頻率，我們提出一個加權技術，以降低偽陽性潛在藥物不良反應案例造成的影響。我們採用全民健康保險研究資料庫進行實驗評估，結果顯示此分析架構及流程，在套用我們所提出的加權技術之下，比起先前的方法有更佳的精確率平均值及平均精確率平均值。

Adverse drug reactions (ADRs) not only have become one of the leading causes of morbidity and mortality but also have impacted significantly on health care costs. Many approaches have been deployed to monitor drug safety, such as spontaneous reporting system (SRS) databases and electronic health record (EHR) databases. SRS databases suffer from a great number of problems that may lead to biased findings, including incomplete information and underreporting, while EHR databases are believed to have the potential to complement the existing SRS databases. In this thesis, we dedicate to the development of a framework which integrates different ADR signal detection methods to discover potential drug-ADR pairs from EHR databases. Based on the frequencies of occurrences of drugs and ADRs, we propose a weighted technique to reduce the influence of false positives in the extracted potential drug-ADR cases. The evaluation on the one real EHR database shows that our framework with the proposed weighted technique outperforms the prior methods in terms of mean of precision and mean average precision.

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