近年來，由於藥物使用量與日俱增，藥物副作用議題逐漸受到重視，此外，隨著新型藥物的推陳出新，新舊藥物的副作用將是未來越來越多人所關切的一項議題。因此，藥物副作用已經成為重要的病人危安問題，且已被證實在未來將佔據絕大多數的病人用藥副作用的事件起因。目前來說，有四種主要用來偵測藥物副作用的來源分別為醫療電子病歷、臨床實驗、自發性的藥物副作用回報系統、以及生物特徵或者化學反應測試。
事實上，最近出現了第五種偵測藥物副作用的主要來源，其為從社群訊息當中來去挖掘網路上所使用的藥物副作用詞彙。
經過實驗結果證實，相比傳統臨床實驗或者線上回報系統，透過此種來源去偵測出特定藥物副作用可以提高找到潛在藥物副作用的機率。因此，這篇論文提出了一個結合文字探勘技術以及關聯演算法的研究架構，為的是要去挖掘出更多的網路使用的藥物副作用詞彙以及透過傳統方法無法找到的那些潛在藥物副作用。

References
[1] Alan, M. H., Stephanie, J. R., Ronald, K. P., Donald, J. O. H. and Kevin, H, “
Using Data Mining to Predict Safety Actions From FDA Adverse Event Reporting System Data,” Drug Information Journal, pp. 41-5, 2007.
[2] Blenkinsopp A, Wilkie P, Wang M, Routledge PA, “Patient reporting of suspected adverse drug reactions: a review of published literature and international experience,” Br J Clin Pharmacol, pp. 148-156, 2007.
[3] Biriell, C. and Edwards, I. R, “ Reasons for reporting adverse drug reactions--some thoughts based on an international review,” Pharmacoepidemiology and Drug Safety, pp. 21-26, 1997.
[4] Chee, B. W., Berlin, R. and Schatz, B, “Predicting adverse drug events from personal health messages,” Annual Symposium proceedings, pp. 217-226. 2011.
[5] DuMouchel, W., “Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System,” The American Statistician, pp. 177-190, 1999.
[6] Edwards, I. R. and Aronson, J. K, “Adverse drug reactions: definitions, diagnosis, and management,” Lancet, pp. 1255-1259, 2000.
[7] Evans, S. J. W., Waller, P. C. and Davis, S, “Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports,” Pharmacoepidemiology and Drug Safety, pp. 483- 486, 2001.
[8] Hakobyan, L., Haaijer-Ruskamp, F. M., de Zeeuw, D., Dobre, D. and Denig, P, “A review of methods used in assessing non-serious adverse drug events in observational studies among type 2 diabetes mellitus patients,” Health and quality of life outcomes, pp. 83-83, 2011.
[9] Hauben, M, “Early postmarketing drug safety surveillance: data mining points to consider,” The Annals of pharmacotherapy, pp. 1625-1630, 2004.
[10] Huidong, Jin., Jie, C., Hongxing, H., Williams, G. J., Kelman, C. and O'Keefe, C. M, “Mining Unexpected Temporal Associations: Applications in Detecting Adverse Drug Reactions,” IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, pp. 488-500, 2008.
[11] Ji, Y., Ying, H., Farber, M. S., Yen, J., Dews, P., Miller, R. E. and Massanari, R. M, “A distributed, collaborative intelligent agent system approach for proactive postmarketing drug safety surveillance,” IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, pp. 826-837, 2010.
[12] Ji, Y. Q., Massanari, R. M., Ager, J., Yen, J., Miller, R. E. and Ying, H, “A fuzzy logic-based computational recognition-primed decision model. Inf. Sci.,” pp. 4338-4353, 2007.
[13] Khanna, N. H. and A., K, “A DIAGNOSTIC DECISION SUPPORT SYSTEM FOR ADVERSE DRUG REACTION USING TEMPORAL REASONING,” AIML Journal, pp. 79-86, 2006.
[14] Kohn, L. T., Corrigan, J. M., and Donaldson, M. S., “To Error is Human: Building a Safer Health System. Washington,” DC: National Academy Press, 1999.
[15] Kubota, K., Koide, D. and Hirai, T., “Comparison of data mining methodologies using Japanese spontaneous reports,” Pharmacoepidemiology and Drug Safety,” pp. 387-394, 2004.
[16] Lanctot, K. L. and Naranjo, C. A., “COMPUTER- ASSISTED EVALUATION OF ADVERSE EVENTS USING A BAYESIAN-APPROACH,” JOURNAL OF CLINICAL PHARMACOLOGY, pp. 142-147, 1994.
[17] Leaman, R.,Wojtulewicz,L., Sullivan, R., Skariah,A., Yang,J., and Gonzalez,G., “Towards Internet-Age Pharmacovigilance: Extracting Adverse Drug Reactions from User Posts to Health-Related Social Networks,” Proceedings of the 2010 Workshop on Biomedical Natural Language Processing, pp. 117–125, 2010.
[18] Lindquist, M., Edwards, I. R., Bate, A., Fucik, H., Nunes, A. M. and Stahl, M., “From association to alert - A revised approach to international signalanalysis. Pharmacoepidemiol,” Drug Safety, pp. 15-25, 1999.
[19] Pouliot, Y., Chiang, A. P. and Butte, A. J., “Predicting Adverse Drug Reactions Using Publicly Available PubChem BioAssay Data,” Clinical Pharmacology & Therapeutics, pp. 90-99, 2011.
[20] Szarfman, A., Tonning, J. M. and Doraiswamy, P. M., “Pharmacovigilancein the 21st century: New systematic tools for an old problem,” Pharmacotherapy, pp. 1099-1104, 2004.
[21] van Puijenbroek, E. P., Egberts, A. C. G., Meyboom, R. H. B. and Leufkens, H. G. M., “Signalling possible drug– drug interactions in a spontaneous reporting system: delay of withdrawal bleeding during concomitant use of oral contraceptives and itraconazole,” Br J Clin Pharmacol, pp. 689-693, 1999.
[22] Wu, Y. T. and Makuch, R. W., “Detecting rare adverse events in postmarketing studies: Sample size considerations,” DRUG INFORMATION JOURNAL, pp. 89-98, 2006.
[23] Yanqing, J., Hao, Y., Dews, P., Farber, M. S., Mansour, A., Tran, J., Miller, R. E. and Massanari, R. M., “A fuzzy recognition-primed decision model-based causal association mining algorithm for detecting adverse drug reactions in postmarketing surveillance,” IEEE, 2010.
[24] Yanqing, J., Hao, Y., Dews, P., Mansour, A., Tran, J., Miller, R. E. and Massanari, R. M., “A Potential Causal Association Mining Algorithm for Screening Adverse Drug Reactions in Postmarketing Surveillance,” IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, pp. 428-437, 2011.
[25] Zeng, Q. T., & Tse, T., “Exploring and developing consumer health vocabularies. Journal of the American Medical Informatics Association,” JAMIA, pp. 24-29, 2006.
[26] Andreea Farcas and Marius Bojita, “Adverse Drug Reactions in Clinical Practice: a Causality Assessment of a Case of Drug-Induced Pancreatitis,” J Gastrointestin Liver Dis, Vol. 18 No 3, pp. 353-358, 2009.
[27] Alison Pope, Clive Adams, Carol Paton, Tim Weaver and Thomas R. E. Barnes, “Assessment of adverse effects in clinical studies of antipsychotic medication: survey of methods used,” BJP, pp. 67-72, 2010.
[28] MARISSAN.D. LASSERE and KENT R. JOHNSON, “Challenges and Progress in Adverse Event Ascertainment and Reporting in Clinical Trials,” The Journal of Rheumatology, vol. 32, no. 10, 2005.
[29] Mei Liu, Eugenia Renne McPeek Hinz, Michael Edwin Matheny, Joshua C Denny, Jonathan Scott Schildcrout, Randolph A Miller, and Hua Xu, “Comparative analysis of pharmacovigilance methods in the detection of adverse drug reactions using electronic medical records,” Liu M, et al. J Am Med Inform Assoc, 2013.
[30] Pernille Warrer, Ebba Holme Hansen, Lars Juhl-Jensen & Lise Aagaard, “Using text-mining techniques in electronic patient records to identify ADRs from medicine use,” British Journal of Clinical Pharmacology, 2011.
[31] Mei Liu, Michael E. Matheny, Yong Hu, Hua Xu, “Data Mining Methodologies for Pharmacovigilance,” SIGKDD Explorations, 2012.
[32] Mei Liu, Yonghui Wu, Yukun Chen, Jingchun Sun, Zhongming Zhao, Xue-wen Chen, Michael Edwin Matheny, Hua Xu, “Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs,” 2012.
[33] Andreas Bender, Daniel W. Youngb, Jeremy L. Jenkinsa, Martin Serranoc, Dmitri Mikhailova, Paul A. Clemonsc and John W. Daviesa, “Chemogenomic Data Analysis: Prediction of Small-Molecule Targets and the Advent of Biological Fingerprints,” Bentham Science Publishers Ltd, 2007.
[34] Gary Saunders, Sasa Ivkovic, Ranadhir Ghosh, John Yearwood, “Applying Anatomical Therapeutic Chemical (ATC) and Critical Term Ontologies to Australian Drug Safety Data for Association Rules and Adverse Event Signalling,” Conferences in Research and Practice in Information Technology, Vol. 58, 2005.
[35] Novia Nurain, Moin Mostakim, Chowdhury Mofizur, “A Review of Different Available Data Sources and Its Limitations for Applying Data Mining Techniques in Pharmacovigilance,” International Journal of Computer Applications & Information Technology Vol. 4, 2013.
[36] Christopher C. Yang, Ling Jiang, Haodong Yang, Xuning Tang, “Detecting Signals of Adverse Drug Reactions from Health Consumer Contributed Content in Social Media,” 2012.