臺灣博碩士論文加值系統

藥物的使用是為了治療疾病，但在合理用藥之情況下仍可能發生無法預期之藥物不良反應(Adverse drug reaction, ADR)。藥物不良反應的發生影響民眾的健康，即使國內提供全國藥物不良反應通報機制，仍有眾多因素造成藥物不良反應通報系統效率低，且通報過程耗費時間長。過往研究多使用藥物不良反應通報系統之資料作為分析目標，通報系統常有漏報(under reporting)的情形，且資料來源缺乏專業醫療判斷，本研究使用全民健康保險研究資料(National Health Insurance Research Database, NHIRD)，資料數量龐大且皆為醫師診斷，將更具參考性。
本研究在藥物上市一段時間後，針對單一藥物成分使用時間關聯規則產生可疑不良反應清單，並使用卡方獨立性檢定協助篩選可疑不良反應清單，建立新藥上市後之藥物監視處理流程。分析結果顯示，本研究產出之藥物不良反應清單能有效排除一般性疾病，並藉由關聯規則排序及卡方檢定有效縮減可疑不良反應數量。期望在藥物不良反應被動通報前就給予相關主管機關、藥學專業人士、藥品廠商參考依據，同時提供醫師針對臺灣人口之投藥參考。

The purpose of using drugs is to treat or prevent disease, but it still may have some unexpected adverse drug reactions (ADRs) which are harmful to people’s health. Though government provides National Adverse Drug Reactions Reporting System in Taiwan, many reasons make the reporting system inefficient and it take long time to report. Most of previous researches used data from National Adverse Drug Reactions Reporting System as analysis target. However, under reporting often happened and the source of the data lacked professional medical judgement. This research chooses National Health Insurance Research Database as analysis target, which is more reliable due to its huge dataset and authoritative prescription by doctors.
This research form suspect ADR lists of ingredients in single drug by temporal association rule after it appear on the market. Besides, it uses chi-square test to filter results then establish the supervise process when new drugs appear on the market. The results can exclude common disease by the suspect ADR lists efficiently and decrease amount of ADRs by the ranking of temporal association rule and chi-square test.

誌謝 i
摘要 ii
ABSTRACT iii
目錄 iv
圖目錄 vii
表目錄 viii
Chapter 1 緒論 1
1.1 研究背景及動機 1
1.2 研究目的 3
1.3 研究架構 3
Chapter 2 文獻探討 4
2.1 藥物不良反應自動偵測 4
2.2 全民健康保險研究資料庫 5
2.2.1 2010年承保抽樣歸人檔（LHID2010） 6
2.3 預期外之藥物不良反應 7
Chapter 3 研究方法 8
3.1 研究方法總覽 8
3.2 資料來源 9
3.2.1 全民健康保險研究資料庫 9
3.2.2 健保用藥品項 9
3.2.3 國際疾病分類代碼 ICD9 10
3.3 資料準備 10
3.3.1 就診診斷疾病與處方藥物 10
3.3.2 目標藥物 11
3.4 時間關聯規則 11
3.4.1 MUTARA 演算法 12
3.4.2 HUNT演算法 16
3.4.3 時間區間 16
3.5 疾病排除 16
3.5.1 篩選有效ICD9疾病編碼 16
3.5.2 卡方獨立性檢定 17
Chapter 4 研究成果 18
4.1 敘述性統計分析 18
4.1.1 資料總數 18
4.1.2 總人數與各藥品服藥人數 18
4.2 卡方檢定: 篩選可疑不良反應清單 20
4.3 關聯規則 22
4.3.1 allopurinol 22
4.3.2 phenytoin 25
4.3.3 carbamazepine 28
4.3.4 lamotrigine 30
4.3.5 diclofenac 33
Chapter 5 結論與建議 37
5.1 研究貢獻 37
5.2 研究限制 37
5.2.1 服藥假設與藥物選擇 37
5.2.2 疾病碼資料 37
5.2.3 專業解讀與關聯規則數量 38
5.3 未來展望 38
參考文獻 39

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