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???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
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dc.contributor.advisor | 盧信銘(Hsin-Min Lu) | |
dc.contributor.author | Cheng-Jen Lee | en |
dc.contributor.author | 李承錱 | zh_TW |
dc.date.accessioned | 2021-05-15T17:54:45Z | - |
dc.date.available | 2015-07-29 | |
dc.date.available | 2021-05-15T17:54:45Z | - |
dc.date.copyright | 2014-07-29 | |
dc.date.issued | 2014 | |
dc.date.submitted | 2014-07-22 | |
dc.identifier.citation | Bate, A., & Evans, S. J. W. (2009). Quantitative signal detection using spontaneous ADR reporting. Pharmacoepidemiology and Drug Safety, 18(6), 427–436. doi:10.1002/pds.1742
Bate, A., Lindquist, M., Edwards, I. R., & Orre, R. (2002). A data mining approach for signal detection and analysis. Drug Safety: An International Journal of Medical Toxicology and Drug Experience, 25(6), 393–397. Bates, D. W. (1995). Incidence of Adverse Drug Events and Potential Adverse Drug Events: Implications for Prevention. JAMA, 274(1), 29. doi:10.1001/jama.1995.03530010043033 Bright, T. J., Wong, A., Dhurjati, R., Bristow, E., Bastian, L., Coeytaux, R. R., … Lo-bach, D. (2012). Effect of clinical decision-support systems: a systematic review. Annals of Internal Medicine, 157(1), 29–43. doi:10.7326/0003-4819-157-1-201207030-00450 Bureau of National Health Insurance, Department of Health, Executive Yuan, Taiwan. (2012, May). Universal Health Coverage in Taiwan. Retrieved from http://www.nhi.gov.tw/Resource/webdata/21717_1_20120808UniversalHealthCoverage.pdf Choi, C. A., Chang, M. J., Choi, H. D., Chung, W.-Y., & Shin, W. G. (2013). Applica-tion of a drug-interaction detection method to the Korean National Health In-surance claims database. Regulatory Toxicology and Pharmacology: RTP, 67(2), 294–298. doi:10.1016/j.yrtph.2013.08.009 Coloma, P. M., Trifiro, G., Schuemie, M. J., Gini, R., Herings, R., Hippisley-Cox, J., … EU-ADR Consortium. (2012). Electronic healthcare databases for active drug safety surveillance: is there enough leverage? Pharmacoepidemiology and Drug Safety, 21(6), 611–621. doi:10.1002/pds.3197 Cornelius, V. R., Sauzet, O., & Evans, S. J. W. (2012). A signal detection method to detect adverse drug reactions using a parametric time-to-event model in simu-lated cohort data. Drug Safety: An International Journal of Medical Toxicology and Drug Experience, 35(7), 599–610. doi:10.2165/11599740-000000000-00000 Deshpande, G., Gogolak, V., & Smith, S. W. (2010). Data Mining in Drug Safety: Re-view of Published Threshold Criteria for Defining Signals of Disproportionate Reporting. Pharmaceutical Medicine, 24(1), 37–43. doi:10.1007/BF03256796 Dumouchel, W. (1999). Bayesian Data Mining in Large Frequency Tables, with an Ap-plication to the FDA Spontaneous Reporting System. The American Statistician, 53(3), 177–190. doi:10.1080/00031305.1999.10474456 Hacker, M. P., Messer, W. S., & Bachmann, K. A. (2009). Pharmacology principles and practice. Amsterdam; Boston: Academic Press/Elsevier. Retrieved from http://public.eblib.com/EBLPublic/PublicView.do?ptiID=452816 Harpaz, R., Chase, H. S., & Friedman, C. (2010). Mining multi-item drug adverse effect associations in spontaneous reporting systems. BMC Bioinformatics, 11(Suppl 9), S7. doi:10.1186/1471-2105-11-S9-S7 Harpaz, R., Vilar, S., Dumouchel, W., Salmasian, H., Haerian, K., Shah, N. H., … Friedman, C. (2013). Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions. Journal of the American Medical Informatics Association: JAMIA, 20(3), 413–419. doi:10.1136/amiajnl-2012-000930 Jha, A. K., Kuperman, G. J., Teich, J. M., Leape, L., Shea, B., Rittenberg, E., … Bates, D. W. (1998). Identifying adverse drug events: development of a comput-er-based monitor and comparison with chart review and stimulated voluntary report. Journal of the American Medical Informatics Association: JAMIA, 5(3), 305–314. Jin, H., Chen, J., He, H., Kelman, C., McAullay, D., & O’Keefe, C. M. (2010). Signal-ing Potential Adverse Drug Reactions from Administrative Health Databases. IEEE Transactions on Knowledge and Data Engineering, 22(6), 839–853. doi:10.1109/TKDE.2009.212 Johansson, S., Wallander, M.-A., de Abajo, F. J., & Garcia Rodriguez, L. A. (2010). Prospective drug safety monitoring using the UK primary-care General Practice Research Database: theoretical framework, feasibility analysis and extrapolation to future scenarios. Drug Safety: An International Journal of Medical Toxicol-ogy and Drug Experience, 33(3), 223–232. doi:10.2165/11319010-000000000-00000 Johnson, J. A. (1995). Drug-Related Morbidity and Mortality: A Cost-of-Illness Model. Archives of Internal Medicine, 155(18), 1949. doi:10.1001/archinte.1995.00430180043006 Kubota, K., Koide, D., & Hirai, T. (2004). Comparison of data mining methodologies using Japanese spontaneous reports. Pharmacoepidemiology and Drug Safety, 13(6), 387–394. doi:10.1002/pds.964 Kuperman, G. J., Bobb, A., Payne, T. H., Avery, A. J., Gandhi, T. K., Burns, G., … Bates, D. W. (2007). Medication-related clinical decision support in computer-ized provider order entry systems: a review. Journal of the American Medical Informatics Association: JAMIA, 14(1), 29–40. doi:10.1197/jamia.M2170 Lazarou, J., Pomeranz, B. H., & Corey, P. N. (1998). Incidence of adverse drug reac-tions in hospitalized patients: a meta-analysis of prospective studies. JAMA: The Journal of the American Medical Association, 279(15), 1200–1205. Minino, A. M., Murphy, S. L., Xu, J., & Kochanek, K. D. (2011). Deaths: final data for 2008. National Vital Statistics Reports: From the Centers for Disease Control and Prevention, National Center for Health Statistics, National Vital Statistics System, 59(10), 1–126. National Health Insurance Administraction, Ministry of Health and Welfare. (2014, January 3). 藥品代碼與ATC碼對照. Retrieved from http://www.nhi.gov.tw/Resource/webdata/13733_1_藥品代碼與ATC碼對照-10212(上網).xls Noren, G. N., Sundberg, R., Bate, A., & Edwards, I. R. (2008). A statistical methodol-ogy for drug-drug interaction surveillance. Statistics in Medicine, 27(16), 3057–3070. doi:10.1002/sim.3247 Orre, R., Lansner, A., Bate, A., & Lindquist, M. (2000). Bayesian neural networks with confidence estimations applied to data mining. Computational Statistics & Data Analysis, 34(4), 473–493. doi:10.1016/S0167-9473(99)00114-0 Park, M. Y., Yoon, D., Lee, K., Kang, S. Y., Park, I., Lee, S.-H., … Park, R. W. (2011). A novel algorithm for detection of adverse drug reaction signals using a hospital electronic medical record database. Pharmacoepidemiology and Drug Safety, 20(6), 598–607. doi:10.1002/pds.2139 Pharmaceutical data mining: approaches and applications for drug discovery. (2010). Hoboken, N.J: Wiley. Sauzet, O., Carvajal, A., Escudero, A., Molokhia, M., & Cornelius, V. R. (2013). Illus-tration of the weibull shape parameter signal detection tool using electronic healthcare record data. Drug Safety: An International Journal of Medical Toxi-cology and Drug Experience, 36(10), 995–1006. doi:10.1007/s40264-013-0061-7 Uppsala Monitoring Centre. (2013, April 22). VigiBase. Retrieved from http://www.who-umc.org/DynPage.aspx?id=98082&mn1=7347&mn2=7252&mn3=7322&mn4=7326 World Health Organization, & WHO Collaborating Centre for International Drug Mon-itoring. (2002). The importance of pharmacovigilance. [Geneva]: World Health Organization : Uppsala Monitoring Centre, WHO Collaborating Centre for In-ternational Drug Monitoring. 蔡雅婷, 陳文雯, & 蔡翠敏. (2014). 102 年度藥品不良品通報系統之案件分析. Drug Safety Newsletter, (45), 18–27. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/5269 | - |
dc.description.abstract | 藥物不良反應(Adverse Drug Reactions,簡稱ADRs)係指接受藥物治療後所產生的嚴重健康危害。更由於ADRs是當今主要死因之一,故妥善監視上市後藥物成為一重要課題。然而,傳統的失衡分析法(disproportionality analysis)與貝氏偵測方法(Bayesian signal detection)仰賴預先收集的ADR通報案例,以及需事先定義、無統一標準的門檻值,其偵測結果也經常無法一致。另一方面,用以進行偵測的資料集長久受限於兩個資料庫—美國FDA之FAERS與WHO之VigiBase,於這些資料庫的偵測也存在諸多困難。
為解決上述問題,本研究使用全民健康保險研究資料庫,以一週為單位聚合每位病患之歷史就診紀錄後,建立藥物與診斷先後關係。我們並提出一結合三種偵測分數:回歸t值(REG)、通報相對比例值(PRR)與通報相對勝算比(ROR)作為輸入特徵的新模型,用以偵測藥物不良反應。實驗結果顯示,相較單獨使用一種分數,結合三種偵測分數的新模型之準確度(Accuracy)最高有9.5%的提升。 | zh_TW |
dc.description.abstract | Adverse Drug Reactions (ADRs) are fatal health problems due to medical treatments. ADRs are leading cause of death, and thus it is crucial to properly monitor post-marketing drugs. However, traditional disproportionality analysis and Bayesian signal detection depend on pre-collected ADR reports and a not universal, predefined threshold; the results are often inconsistent. Moreover, the available data sources were limited to two databases — U.S. FDA’s FAERS and WHO’s VigiBase; there are also several difficulties when detecting ADRs in these databases.
To address above problems, in this study, we proposed a model combining three detecting scores: regression’s t-value (REG), proportional reporting ratio (PRR), and reporting odds ratio (ROR), as features for detecting serious drug-ADR pairs from one-week aggregated patient-week information with precedence relationship between drugs and diagnoses, in an health insurance claims database NHIRD (National Health Insurance Research Database). We demonstrated that the proposed combined score led to an improvement (up to 9.5%) of signal detection accuracy over applying each of score independently. | en |
dc.description.provenance | Made available in DSpace on 2021-05-15T17:54:45Z (GMT). No. of bitstreams: 1 ntu-103-R01725015-1.pdf: 2138051 bytes, checksum: 368a745d6b69c48ead20bbff1762b478 (MD5) Previous issue date: 2014 | en |
dc.description.tableofcontents | Table of Contents i
List of Tables iii List of Figures iv Chapter 1 Introduction 1 Chapter 2 Literatures Review 4 2.1 ADR Signal Detection 4 2.1.1 Disproportionality Analysis (DPA) 6 2.1.2 Bayesian Signal Detection (BSD) 7 2.1.3 The Problems of Traditional Approaches for ADR Detection 9 2.1.4 Other Approaches for ADR Detection 10 2.2 The Data Sources Used for ADR Detection 11 2.3 Evaluating Performance in ADR Detection 12 Chapter 3 Data and Models 15 3.1 Data Source 16 3.2 Patient Week Aggregation 18 3.3 Feature Generation 19 3.4 Reference Standard 24 3.5 Evaluation 25 Chapter 4 Results 27 4.1 Signaling Performance 27 4.2 Marginal Improvement of Combined Model 28 4.3 Differences between ADRs 29 4.4 The Effect of Period Length 29 Chapter 5 Conclusion 31 5.1 Contributions 31 5.2 Managerial Implication 31 5.3 Limitations and Future Work 32 References 33 | |
dc.language.iso | en | |
dc.title | 於健保資料庫中偵測藥物不良反應 | zh_TW |
dc.title | Detecting Adverse Drug Reactions in Health Insurance Claims Data | en |
dc.type | Thesis | |
dc.date.schoolyear | 102-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 曹承礎(Seng-Cho Chou),李昇暾(Sheng-Tun Li) | |
dc.subject.keyword | 藥物不良反應,訊號偵測,健康資料庫,藥物安全監視,藥物主動監視, | zh_TW |
dc.subject.keyword | adverse drug reaction,signal detection,administrative health database,drug safety surveillance,pharmacovigilance, | en |
dc.relation.page | 37 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2014-07-22 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
Appears in Collections: | 資訊管理學系 |
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