史蒂芬強生症是一種罕見且嚴重的疾病，其特徵為表皮的細胞死亡、真皮與表皮分離，是一種可致命的皮膚疾病。引起史蒂芬強生症的因素很多種，其中大多數病例為不當使用藥物所導致。會引起史蒂芬強生症藥物廣泛分佈於各種不同的醫療用途，然而只有極少數的醫師熟悉這些藥物，導致時常有使用SJS 用藥而導致得病的案例。
本研究使用台灣全民健保資料庫，以某一年份之史蒂芬強生症病患為例。藉由資料探勘分析病患資料，把病患的就醫與用藥記錄，透過關聯法則找出資料間的相關關係。根據關聯法則中的序列模式，把病患就醫與用藥記錄以序列模式呈現，並運用GSP、PrefixSpan 演算法找出高頻率的就醫與用藥序列，所得結果可作為醫療上的參考。

Stevens-Johnson syndrome (SJS), a form of toxic epidermal necrolysis, is a lifethreatening skin condition, in which cell death causes the epidermis to separate from the dermis. There are many factors cause SJS, most of SJS cases are caused by using improper drugs. The drugs which cause SJS are used in various medical fields. However, very few physician are familiar with all these drugs, so there are many cases when physician is not aware about the drugs causing SJS.
This study uses 1-year data of Taiwan’s NHIRD (National Health Insurance Research Database) to find the sequential pattern of medical specialties and their prescription for SJS patients. Two sequential patterns mining algorithms, GSP and PrefixSpan are used to find patterns. The research results could be use as medical reference.

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