藥物交互作用，輕則產生副作用或導致治療效果的降低，重者甚至會造成死亡或引發致命性的危險，這個問題確實值得引起大家的關注，尤其台灣醫師在開藥數目上較國外醫師來的多，引發藥物交互的機率也因而來的更高，如果能透過數據將此問題的嚴重性表露出來，且對此問題提出對策，對國內醫療品質的提升會是一大幫助。 本研究的資料來源為中央健保局資料庫，資料選定範圍為每年就診率最高的呼吸道疾病，想藉由統計分析和資料採礦方法分析，從這龐大的資料中，得到與藥物交互作用相關的有用數據，並將這些數據轉換成訊息，讓大家了解。 在統計分析的部分，從各個不同的角度來探討上呼吸道疾病的藥物交互作用現象，包括病患的基本資料，如：性別、年齡…，就醫行為，如：就醫月份、就分，開藥行為，如：開藥日份、開藥品項；至於 資料採礦分析的部分，則是利用C5.0決策樹，來找出高危險群；最後，針對整體中最容易產生高危險交互作用的藥物組，對其藥效、藥物機轉…等做說明。 / Drug interactions, to a lesser extent, could cause side effects or reduce the drug efficiency, to a greater extent, is life-threatening or could lead to fatality. This issue deserves the attention from the medical personnel and us, the users. The prescription practice in Taiwan more often than not gives out relatively high doses of medications than those of the practice in North America. Consequently the risk of having drug interactions increases respectively. If we could bring the attention of the related party into this alarming situation with the supporting statistical numbers, then hopefully we could set off the stage for a better medical treatment in Taiwan. We used the data from National Health Insurance database, selecting data only on the patients contracting respiratory system disease which sit for the largest portion in the database. We examine the data using statistical and data mining techniques in an attempt to extract the data on drug interactions and turn the number into useful information for the benefit of all related parties. In light of the statistical analysis, we have in depth analysis of the drug interactions on medications used in upper respiratory tract from different perspective such as patient’s record which includes age, the frequency in seeing the physician, details on drug prescription. As for the analysis, data mining technique used is C5.0 decision tree in determining the group at risk for getting the side effects of drug interactions. Moreover, we also have summarized the list of drugs that have shown the tendency to induce drug interaction so that extra caution should be taken when administering these groups of medications.

全球化時代與資訊化社會的來臨，電腦與網際網路成為生活中不可或缺的要素，儘管至2008年為止，我國有將近七成的民眾透過網路科技享受到更多的便利性，但社會上仍存在著數位落差（digital divide）的問題，數位落差除了使得資訊窮人（information-poor）不易取得資訊，亦將對其經濟、人權等各方面造成影響。故研究目的在利用資料採礦的應用，配合SPSS Clementine 12.0的軟體，探討數位落差的現象，並嘗試找出形成數位落差的影響原因。 　　本研究主要投入人口統計變數以及生活型態變數，並藉由C5.0決策樹、C&RT分類樹，以及CHAID分類樹建立模型，透過這三個分類迴歸樹的模型，發現到「年齡」、「教育程度」、「地理區域」、「個人資產狀況」、「經濟主要來源：子女」、「個人每月可支配所得」以及「收入來源：薪資」共七項變數同時對民眾是否成為數位落差中的資訊富人（information-rich）有著較重要的影響性，因此，研究最後依據此七項進行政策建議，以提供相關單位之參考。 / In this globalized and informational society, computers and internet networks are essential elements in our daily lives. Until the year 2008, almost 70% of population in Taiwan has enjoyed greater conveniences through networking technologies. However, the issue of “digital divide” remains, where information-poor cannot obtain information easily, and the issue affects the society in terms of economies and human rights. Consequently, the purpose of this research is aimed to find the reasons behind “digital divide” using data-mining techniques with SPSS Clementine 12.0 statistical software. 　　The research will input demographic variables and life-style variables. Using C5.0 decision tree, C&RT tree, and CHAID methodologies to build model, and subsequently discovers that whether the 7 variables - “age”, “level of education”, “location”, “personal asset status”, “main source of income: children”, “monthly personal disposal income” and “source of income: salary” will have significant impacts on information-rich population within “digital divide”. Therefore, the research recommendations will be provided according to the results from these 7 variables.