隨著科技的日新月異，資料蒐集以及儲存方法的進步，因此能夠快速累積龐大的資料，然而如何能從大量的資料，萃取有用的資訊，甚至發現一些新奇以及有用的樣式，是一大課題。其中資料探勘即是一種能夠在大量資料自動化發現一些有用資訊的流程，且過去資料探勘的發展，已廣泛的被應用在各個知識領域中如醫療、工程、科學等。本研究希望發展一套有效的醫師用藥行為探勘模式，用以探討具有嚴重用藥併發症之疾病，會對醫師之用藥行為產生怎樣的變化。
本研究以全民健保資料庫，某一年份之史蒂芬氏強生症候群病人為例，希望以病人住院前、後用藥的觀點，藉由資料探勘的手法，透過以規則為基礎之分類的方法，將病人做分類，發現病人之用藥行為，除此之外，將病人之用藥以矩陣表示，透過F-measure相似度的計算，結合凝聚式的階層分群演算法，將用藥模式相似的病人做分群，再透過序列性關聯，發現各群的特徵用藥序列，做為醫生開藥的方針。其中病人用藥矩陣相似度的計算，可先將用藥相似病人歸為一群，如此可避免從大量資料中，透過序列性關聯，找出過多無意義之特徵用藥序列。

In clinical practice, a severe illness might be the result of an untoward reaction to the pharmacological treatment for another disease. For instance, Stevens-Johnson syndrome (SJS) can be caused by antibiotics; nonsteroidal anti-inflammatory drugs; antiepileptics; antugout agents; and cardiovascular drugs, as well as the drugs for local use in ophthalmology and dermatology. Since there are so many drugs covering various different medical fields, very few physicians are familiar with all the potential drugs that can cause SJS. Hence new cases or even recurrent cases of drug-induced SJS are still reported from time to time.
Currently in Taiwan, the causal relations between physicians’ prescription behaviors and the occurrence of SJS are not clear. Therefore, in this research, by applying the SJS cases data from National Health Insurance database, a pharmacological treatment mining approach is proposed to investigate physicians’ prescription behaviors before and after the occurrence of SJS.
This research will describe the behavior of pharmacological treatment by the rule-based classification method. A matrix will be used to specify the pharmacological record for each SJS case. Then, a modified F-measure will be used to measure the similarity between each pair of matrices. The measured data will be used to cluster the cases. For each group of cases, its frequent item sets will be mined and the behavior pattern of the pharmacological treatment will be identified.

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