請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98322完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳舜文 | zh_TW |
| dc.contributor.advisor | Shun-Wen Wu | en |
| dc.contributor.author | 周義發 | zh_TW |
| dc.contributor.author | Yi Fa Zhou | en |
| dc.date.accessioned | 2025-08-01T16:12:58Z | - |
| dc.date.available | 2025-08-02 | - |
| dc.date.copyright | 2025-08-01 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-07-29 | - |
| dc.identifier.citation | 參 考 文 獻
壹、中文部分 王曉琴、楊震、包城、郭松柏、許傳青,2025,〈基於SARIMA-LSTM模型的中國肺結核傳染病預測研究〉,《統計學與應用》,14(2), 8-21。 朱玉、夏結來、王靜,2009,〈單純ARIMA模型和ARIMA-GRNN組合模型在猩紅熱發病率中的預測效果比較〉,《中華流行病學雜誌》,30(9), 964-968。 朱星月、林騰飛、米源、胡明,2018,〈間斷時間序列模型及其在衛生政策干預效果評價中的應用〉,《中國藥事》,32(11), 1531-1540。https://doi.org/10.16153/j.1002-7777.2018.11.013 易力、余新華,2019,〈美國醫療器械唯一標識(UDI)系統實施進展〉,《中國醫藥導刊》,21(9), 511-515。 岳惠麗,2009,〈我國居民消費價格指數時間序列預測——基於ARIMA模型與平滑ARIMA模型的比較分析〉,《北方經貿》,8, 9-10。 馬愛霞、謝靜、唐文熙,2018,〈ARIMA模型、BP神經網路及其組合模型在衛生政策評估中的實證比較:以公立醫院價格改革為例〉,《中國衛生政策研究》,11(1), 76-83。 郭靜利、董渤,2019,〈基於SARIMA模型的國際稻米價格預測〉,《價格理論與實踐》,2019(1), 79-82。 張喜紅、李慧、曹文君、崔永梅,2018,〈SARIMA模型在長治市肺結核預測中的應用〉,《中國醫科大學學》,47(7), 585-588。 國家藥品監督管理局,2019,《醫療器械唯一標識系統規則》。取自:https://www.gov.cn/gongbao/content/2019/content_5462534.htm(最後流覽日期:2025年3月20日) 國家藥品監督管理局、國家衛生健康委、國家醫保局,2021,《國家藥監局 國家衛生健康委 國家醫保局關於做好第二批實施醫療器械唯一標識工作的公告(2021年第114號)》。取自:https://udi.nmpa.gov.cn/toDetail.html?infoId=63&CatalogId=2(最後流覽日期:2025年7月1日) 國家藥品監督管理局、國家衛生健康委、國家醫保局,2023,《国家药监局 国家卫生健康委 国家医保局关于做好第三批实施医疗器械唯一标识工作的公告(2023年第22号)》。取自:https://www.nmpa.gov.cn/ylqx/ylqxggtg/20230217152350198.html?type=pc&m=(最後流覽日期:2025年7月1日) 黃國寶、黎衍雲、吳菲、沈鑫、徐望紅,2020,〈ARIMA模型和ARIMA-SVM模型對上海市2型糖尿病患者肺結核發病的預測效果〉,《復旦大學學報(醫學版)》,47(6), 899-905。https://doi.org/10.3969/j.issn.1672-8467.2020.06.016 楊仁東、胡世雄、鄧志紅、羅塏煒、彭揚琴、孫振球、曾小敏,2016,〈湖南省手足口病發病趨勢SARIMA模型預測〉,《中國公共衛生》,32(1), 48-52。 賈志濤,2020,〈醫療器械唯一標識(UDI)系統實施探討——基於GS1標準的應用實踐〉,《中國醫藥導刊》,22(3), 201–210。 趙梅、劉維忠,2015,〈ARIMA和平滑ARIMA模型的中國棉花價格短期預測比較〉,《貴州農業科學》,43(11), 206-208。 鄭佳、易力、李靜莉,2018,〈美國醫療器械認可共識標準管理體系研究〉,《中國醫療器械雜誌》,42(2), 119-121, 132。 劉靚、郭媛媛、張文思、陳聰,2022,〈我國醫療器械唯一標識 (UDI) 應用分析與展望〉,《中國醫藥導刊》,24(9), 903–908。 盧鵬飛、須成傑、張敬誼、韓侶、李靜,2019,〈基於SARIMA-LSTM的門診量預測研究〉,《大數據》,5(6), 101-110。 貳、英文部分 Alderman, A., Caplin, D., Hammond, D. C., Keane, A., Turetzky, J., & Kane, W. J. (2023). Clinical results of mentor Memorygel Xtra breast implants from the glow clinical trial. Aesthetic Surgery Journal, 43(12). https://doi.org/10.1093/asj/sjad272 Alemzadeh, H., Raman, J., Leveson, N., Kalbarczyk, Z., & Iyer, R. K. (2016). Adverse events in robotic surgery: A retrospective study of 14 years of FDA Data. PLOS ONE, 11(4). https://doi.org/10.1371/journal.pone.0151470 Aminsharifi, A., Kotamarti, S., Silver, D., & Schulman, A. (2019). Major complications and adverse events related to the injection of the SpaceOAR hydrogel system before radiotherapy for prostate cancer: Review of the manufacturer and User Facility Device Experience Database. Journal of Endourology, 33(10), 868–871. https://doi.org/10.1089/end.2019.0431 Baxter receives FDA final order for colleague infusion pumps recall in US. (2010, July 14). NS Medical Devices. https://www.nsmedicaldevices.com/news/baxter_receives_fda_final_order_for_colleague_infusion_pumps_recall_in_us_100714/ Benyaminpour, S., & Shalom, M. (2024). Optimizing breast implant outcomes: Memorygel Xtra implants and future research directions. Aesthetic Surgery Journal, 44(7). https://doi.org/10.1093/asj/sjae061 Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: A tutorial. International Journal of Epidemiology, 46(1), 348–355. https://doi.org/10.1093/ije/dyw098 Bianchini, E., Francesconi, M., Testa, M., Tanase, M., & Gemignani, V. (2019). Unique device identification and traceability for medical software: A major challenge for manufacturers in an ever-evolving marketplace. Journal of Biomedical Informatics, 93, 103150. https://doi.org/10.1016/j.jbi.2019.103150 Blandford, A., Furniss, D., & Vincent, C. (2014). Patient safety and interactive medical devices: Realigning work as imagined and work as done. Clinical Risk, 20(5), 107–110. https://doi.org/10.1177/1356262214556550 Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1 Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2016). Time series analysis: Forecasting and control. John Wiley & Sons, Inc. Caplin, D. A., Calobrace, M. B., Wixtrom, R. N., Estes, M. M., & Canady, J. W. (2021). Memorygel breast implants: Final safety and efficacy results after 10 years of follow-up. Plastic and Reconstructive Surgery, 147(3), 556–566. https://doi.org/10.1097/prs.0000000000007635 Chang, A. (2015, February 19). “superbug” outbreak raises questions about medical tool. Medical Xpress. https://medicalxpress.com/news/2015-02-dead-exposed-superbug-outbreak-hospital.html Chen, D., & Wang, H. (2011). The stationarity and invertibility of a class of nonlinear arma models. Science China Mathematics, 54(3), 469–478. https://doi.org/10.1007/s11425-010-4160-y Chotikawanich, E., Korman, E., & Monga, M. (2011). Complications of stone baskets: 14-year review of the manufacturer and User Facility Device Experience Database. Journal of Urology, 185(1), 179–183. https://doi.org/10.1016/j.juro.2010.08.091 Clemens, M. W., Jacobsen, E. D., & Horwitz, S. M. (2019). 2019 NCCN consensus guidelines on the diagnosis and treatment of breast implant-associated anaplastic large cell lymphoma (BIA-ALCL). Aesthetic Surgery Journal, 39(Supplement_1). https://doi.org/10.1093/asj/sjy331 Coroneos, C. J., Selber, J. C., Offodile, A. C., Butler, C. E., & Clemens, M. W. (2019). US FDA breast implant postapproval studies: Long‑term outcomes in 99,993 patients. Annals of Surgery, 269(1), 30–36. https://doi.org/10.1097/sla.0000000000002990 Cunningham, B. (2007). The mentor core study on silicone MemoryGel breast implants. Plastic and Reconstructive Surgery, 120(Supplement 1). https://doi.org/10.1097/01.prs.0000286574.88752.04 Dhruva, S. S., Ross, J. S., & Wilson, N. A. (2023). Unique device identifiers for medical devices at 10 Years. JAMA Internal Medicine, 183(10), 1045. https://doi.org/10.1001/jamainternmed.2023.3572 Drozda, J. P., Dudley, C., Helmering, P., Roach, J., & Hutchison, L. (2016). The mercy unique device identifier demonstration project: Implementing point of use product identification in the cardiac catheterization laboratories of a regional health system. Healthcare, 4(2), 116–119. https://doi.org/10.1016/j.hjdsi.2015.07.002 Elmesmari, N., Suliman, R., & Elnazzal, M. (2022). The effect of over-differencing on model validity. Scholars Journal of Physics, Mathematics and Statistics, 9(8), 122–144. https://doi.org/10.36347/sjpms.2022.v09i08.001 Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987–1007. https://doi.org/10.2307/1912773 Ensign, L. G., & Cohen, K. B. (2017). A primer to the structure, content and linkage of the FDA's Manufacturer and User Facility Device Experience (MAUDE) files. EGEMS, 5(1), 12. https://doi.org/10.5334/egems.221 Everhart, A. O., Karaca-Mandic, P., Redberg, R. F., Ross, J. S., & Dhruva, S. S. (2025). Late adverse event reporting from medical device manufacturers to the US Food and Drug Administration: Cross sectional study. BMJ. https://doi.org/10.1136/bmj-2024-081518 Ewusie, J. E., Soobiah, C., Blondal, E., Beyene, J., Thabane, L., & Hamid, J. S. (2020). Methods, applications and challenges in the analysis of Interrupted Time Series data: A scoping review. Journal of Multidisciplinary Healthcare, Volume 13, 411–423. https://doi.org/10.2147/jmdh.s241085 Federal Food, Drug, and Cosmetic Act, 21 U.S.C. (1938). Food and Drug Administration. (2006, November 17). Premarket approval (PMA). AccessData.FDA.gov. https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpma/pma.cfm?ID=P030053 Food and Drug Administration. (2013, September 24). Unique Device Identification System. Federal Register. https://www.federalregister.gov/documents/2013/09/24/2013-23059/unique-device-identification-system Food and Drug Administration. (2014, February 13). Questions and Answers about eMDR - Electronic Medical Device Reporting - Guidance for Industry, User Facilities and FDA Staff. U.S. Food and Drug Administration. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/questions-and-answers-about-emdr-electronic-medical-device-reporting-guidance-industry-user Food and Drug Administration (2015, February 27). If I am a manufacturer, what information must I submit in my individual adverse event reports? Code of Federal Regulations. https://www.ecfr.gov/current/title-21/chapter-I/subchapter-H/part-803/subpart-E/section-803.52 Food and Drug Administration. (2018a, July 11). 80 years of the Federal Food, Drug, and Cosmetic Act. U.S. Food and Drug Administration. https://www.fda.gov/about-fda/fda-history-exhibits/80-years-federal-food-drug-and-cosmetic-act Food and Drug Administration. (2018b, March 28). FDAAA implementation – Highlights one year after enactment. U.S. Food and Drug Administration. https://www.fda.gov/regulatory-information/food-and-drug-administration-amendments-act-fdaaa-2007/fdaaa-implementation-highlights-one-year-after-enactment Food and Drug Administration. (2018c, March 29). Food and Drug Administration amendments act (FDAAA) of 2007. U.S. Food and Drug Administration. https://www.fda.gov/regulatory-information/selected-amendments-fdc-act/food-and-drug-administration-amendments-act-fdaaa-2007 Food and Drug Administration. (2018d, March 22). Medical device reporting regulation history. U.S. Food and Drug Administration. https://www.fda.gov/medical-devices/mandatory-reporting-requirements-manufacturers-importers-and-device-user-facilities/medical-device-reporting-regulation-history Food and Drug Administration. (2018e, July 16). Update on the Safety of Silicone Gel-Filled Breast Implants (2011) - Executive Summary. U.S. Food and Drug Administration. https://www.fda.gov/medical-devices/breast-implants/update-safety-silicone-gel-filled-breast-implants-2011-executive-summary Food and Drug Administration. (2019a, July 24). FDA takes action to protect patients from risk of certain textured breast implants; requests Allergan voluntarily recall certain breast implants and tissue expanders from market. U.S. Food and Drug Administration. https://www.fda.gov/news-events/press-announcements/fda-takes-action-protect-patients-risk-certain-textured-breast-implants-requests-allergan Food and Drug Administration. (2019b, July 28). Forward into the past with FDA’s new history exhibit. U.S. Food and Drug Administration. https://www.fda.gov/news-events/fda-voices/forward-past-fdas-new-history-exhibit Food and Drug Administration. (2023a, August 21). A History of Medical Device Regulation & Oversight in the United States. U.S. Food and Drug Administration. https://www.fda.gov/medical-devices/overview-device-regulation/history-medical-device-regulation-oversight-united-states Food and Drug Administration. (2023b, August 1). Baxter healthcare corporation recalls SIGMA spectrum infusion pumps with master drug library and Spectrum IQ infusion systems with dose IQ safety software for repeat upstream Occlusion false alarms. U.S. Food and Drug Administration. https://www.fda.gov/medical-devices/medical-device-recalls/baxter-healthcare-corporation-recalls-sigma-spectrum-infusion-pumps-master-drug-library-and-spectrum Food and Drug Administration. (2023c, October 19). Benefits of a UDI system. U.S. Food and Drug Administration. https://www.fda.gov/medical-devices/unique-device-identification-system-udi-system/benefits-udi-system Food and Drug Administration. (2023d, December 15). Risks and complications of breast implants. U.S. Food and Drug Administration. https://www.fda.gov/medical-devices/breast-implants/risks-and-complications-breast-implants Food and Drug Administration. (2024a, June 6). About Manufacturer and User Facility Device Experience (Maude). U.S. Food and Drug Administration. https://www.fda.gov/medical-devices/mandatory-reporting-requirements-manufacturers-importers-and-device-user-facilities/about-manufacturer-and-user-facility-device-experience-maude Food and Drug Administration. (2024b, December 17). Global unique device identification database (GUDID). U.S. Food and Drug Administration. https://www.fda.gov/medical-devices/unique-device-identification-system-udi-system/global-unique-device-identification-database-gudid Food and Drug Administration. (2025a, March 27). Medical device reporting (MDR): How to report medical device problems. U.S. Food and Drug Administration. https://www.fda.gov/medical-devices/medical-device-safety/medical-device-reporting-mdr-how-report-medical-device-problems#overview Food and Drug Administration. (2025b, March 27). Voluntary Malfunction Summary Reporting Program. U.S. Food and Drug Administration. https://www.fda.gov/medical-devices/medical-device-reporting-mdr-how-report-medical-device-problems/voluntary-malfunction-summary-reporting-program Leape, L. L., & Berwick, D. M. (2005). Five years after To Err Is Human: What have we learned? JAMA, 293(19), 2384. https://doi.org/10.1001/jama.293.19.2384 Food and Drug Administration Amendments Act of 2007, Pub. L. No. 110-85, 121 Stat. 823 (2007). Food and Drug Administration Safety and Innovation Act, Pub. L. No. 112-144, 126 Stat. 996 (2012). Gross, T. P., & Crowley, J. (2012). Unique device identification in the service of public health. New England Journal of Medicine, 367(17), 1583–1585. https://doi.org/10.1056/nejmp1113608 Heaton, J., Okoh, A., Sossou, C., Singh, S., Sandhu, M., Chakrabarti, R., Rao, R., Waxman, S., Tayal, R., & Wasty, N. (2020). Adverse events after left atrial appendage closure: Lessons learned from the Manufacturer and User Facility Device Experience (maude) database. Journal of Invasive Cardiology, 32(8). https://doi.org/10.25270/jic/20.00042 Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTexts. https://otexts.com/fpp3/ Institute of Medicine. (2000). To Err Is Human: Building a Safer Health System. Washington, DC: The National Academies Press. https://doi.org/10.17226/9728 Jandoc, R., Burden, A. M., Mamdani, M., Lévesque, L. E., & Cadarette, S. M. (2015). Interrupted time series analysis in drug utilization research is increasing: Systematic review and recommendations. Journal of Clinical Epidemiology, 68(8), 950–956. https://doi.org/10.1016/j.jclinepi.2014.12.018 Khalid, N., Rogers, T., Shlofmitz, E., Chen, Y., Musallam, A., Khan, J. M., Iantorno, M., Gajanana, D., Hashim, H., Torguson, R., Bernardo, N., & Waksman, R. (2019). Adverse events and modes of failure related to Impella RP: Insights from the Manufacturer and User Facility Device Experience (MAUDE) Database Cardiovascular Revascularization Medicine, 20(6), 503–506. https://doi.org/10.1016/j.carrev.2019.03.010 Khan, A. R., Tripathi, A., Farid, T. A., Abaid, B., Bhatt, D. L., Resar, J. R., & Flaherty, M. P. (2017). Stent thrombosis with bioabsorbable polymer drug-eluting stents: insights from the Food and Drug Administration database. Coronary Artery Disease, 28(7), 564–569. https://doi.org/10.1097/mca.0000000000000539 Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics, 54(1–3), 159–178. https://doi.org/10.1016/0304-4076(92)90104-y Lalani, C., Kunwar, E. M., Kinard-Tomes, M., Dhruva, S., & Redberg, R. (2021). Quantifying the underreporting of death for cardiovascular devices in the FDA Manufacturer and User Facility Device Experience (maude) database. Journal of the American College of Cardiology, 77(18), 3221. https://doi.org/10.1016/s0735-1097(21)04576-9 Leveson, N. G., & Turner, C. S. (1993). An investigation of the therac-25 accidents. Computer, 26(7), 18–41. https://doi.org/10.1109/mc.1993.274940 Linden, A. (2015). Conducting interrupted time-series analysis for single- and multiple-group comparisons. The Stata Journal, 15(2), 480–500. https://doi.org/10.1177/1536867x1501500208 Linden, A. (2017). A comprehensive set of postestimation measures to enrich interrupted time-series analysis. The Stata Journal, 17(1), 73–88. https://doi.org/10.1177/1536867x1701700105 Ljung, G. M., & Box, G. E. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297. https://doi.org/10.2307/2335207 Loch-Wilkinson, A., Beath, K. J., Knight, R. J., Wessels, W. L., Magnusson, M., Papadopoulos, T., Connell, T., Lofts, J., Locke, M., Hopper, I., Cooter, R., Vickery, K., Joshi, P. A., Prince, H. M., & Deva, A. K. (2017). Breast implant–associated anaplastic large cell lymphoma in Australia and New Zealand: High-surface-area textured implants are associated with increased risk. Plastic and Reconstructive Surgery, 140(4), 645–654. https://doi.org/10.1097/prs.0000000000003654 Maher, J. L., Bennett, D. C., Bennett, L. L., Grothaus, P., & Mahabir, R. C. (2010). Breast augmentation: A geographical comparison. Canadian Journal of Plastic Surgery, 18(4), 44–46. https://doi.org/10.1177/229255031001800405 Medical Device Amendments of 1976, Pub. L. No. 94-295, 90 Stat. 539 (1976). Medical Device Reporting, 21 C.F.R. pt. 803 (1984). Medical Device Tracking Requirements, 21 C.F.R. pt. 821 (1993). Makary, M. A., & Daniel, M. (2016). Medical error—the third leading cause of death in the US. BMJ, 353, i2139. https://doi.org/10.1136/bmj.i2139 McKernan, C. D., Vorstenbosch, J., Chu, J. J., & Nelson, J. A. (2021). Breast implant safety: An overview of current regulations and screening guidelines. Journal of General Internal Medicine, 37(1), 212–216. https://doi.org/10.1007/s11606-021-06899-y Mishali, M., Sheffer, N., Mishali, O., & Negev, M. (2025). Evaluation of reporting trends in the maude database: 1991 to 2022. Digital Health, 11. https://doi.org/10.1177/20552076251314094 Mooghali, M., Ross, J. S., Kadakia, K. T., & Dhruva, S. S. (2023). Characterization of US Food and Drug Administration Class I recalls from 2018 to 2022 for moderate- and high-risk medical devices: A cross-sectional study. Medical devices (Auckl), 16, 111–122. https://doi.org/10.2147/MDER.S412802 Noor, T. H., Almars, A. M., Alwateer, M., Almaliki, M., Gad, I., & Atlam, E.-S. (2022). SARIMA: A Seasonal Autoregressive Integrated Moving Average Model for Crime Analysis in Saudi Arabia. Electronics, 11(23), 3986. https://doi.org/10.3390/electronics11233986 Parker Waichman LLP. (n.d.). Baxter colleague infusion pump recall. https://www.yourlawyer.com/infusion-pumps/baxter-colleague-infusion-pump-recall/ Patel, N. H., Schulman, A. A., Bloom, J. B., Uppaluri, N., Phillips, J. L., Konno, S., Choudhury, M., & Eshghi, M. (2017). Device-related adverse events during percutaneous nephrolithotomy: Review of the Manufacturer and User Facility Device Experience database. Journal of Endourology, 31(10), 1007–1011. https://doi.org/10.1089/end.2017.0343 Perone, G. (2022). Using the SARIMA Model to Forecast the Fourth Global Wave of Cumulative Deaths from COVID-19: Evidence from 12 Hard-Hit Big Countries. Econometrics, 10(2), 18. https://doi.org/10.3390/econometrics10020018 Peters, W., Pellerin, C., & Janney, C. (2020). Research: Evaluation of orthopedic hip device recalls by the FDA from 2007 to 2017. Biomedical Instrumentation & Technology, 54(6), 418–426. https://doi.org/10.2345/0899-8205-54.6.418 Ramsay, C. R., Matowe, L., Grilli, R., Grimshaw, J. M., & Thomas, R. E. (2003). Interrupted time series designs in health technology sssessment: Lessons from two systematic reviews of behavior change strategies. International Journal of Technology Assessment in Health Care, 19(4), 613–623. https://doi.org/10.1017/s0266462303000576 Rathi, V. K., Ross, J. S., & Redberg, R. F. (2023). Unique device identifiers—missing in action. JAMA Internal Medicine, 183(10), 1049. https://doi.org/10.1001/jamainternmed.2023.3561 Regulation (EU) 2017/745 of the European Parliament and of the Council of 5 April 2017 on medical devices. (2017). Official Journal of the European Union, L 117, 1–175. https://eur-lex.europa.eu/eli/reg/2017/745/oj Regulation (EU) 2017/746 of the European Parliament and of the Council of 5 April 2017 on in vitro diagnostic medical devices. (2017). Official Journal of the European Union, L 117, 176–332. https://eur-lex.europa.eu/eli/reg/2017/746/oj Rising, J., & Moscovitch, B. (2014). The Food and Drug Administration’s unique device identification system. JAMA Internal Medicine, 174(11), 1719. https://doi.org/10.1001/jamainternmed.2014.4195 Safe Medical Devices Act of 1990, Pub. L. No. 101-629, 104 Stat. 4511 (1990). Salazar, L. (2021, October 26). Addressing the medical device safety crisis. The Regulatory Review. https://www.theregreview.org/2021/10/27/salazar-addressing-medical-device-safety-crisis/ Sandberg, J. M., Gray, I., Pearlman, A., & Terlecki, R. P. (2018). An evaluation of the Manufacturer and User Facility Device Experience database that inspired the United States Food and Drug Administration’s reclassification of transvaginal mesh. Investigative and Clinical Urology, 59(2), 126. https://doi.org/10.4111/icu.2018.59.2.126 Schaffer, A. L., Dobbins, T. A., & Pearson, S.-A. (2021). Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: A guide for evaluating large-scale health interventions. BMC Medical Research Methodology, 21(1). https://doi.org/10.1186/s12874-021-01235-8 Silvestrini, E. (2024, March 21). da Vinci robotic surgery complications - risks and malfunction. Drugwatch. https://www.drugwatch.com/davinci-surgery/complications/ Short, K. K., Wixtrom, R. N., Estes, M. M., Leopold, J., & Canady, J. W. (2021). Results from the Memorygel post-approval study. Plastic and Reconstructive Surgery - Global Open, 9(3). https://doi.org/10.1097/gox.0000000000003402 Stadnytska, T., Braun, S., & Werner, J. (2008). Comparison of automated procedures for Arma model identification. Behavior Research Methods, 40(1), 250–262. https://doi.org/10.3758/brm.40.1.250 Stevens, G. W., Pacella, S. J., Gear, A. J., Freeman, M. E., McWhorter, C., Tenenbaum, M. J., & Stoker, D. A. (2008). Clinical experience with a fourth-generation textured silicone gel breast implant: A review of 1012 mentor Memorygel breast implants. Aesthetic Surgery Journal, 28(6), 642–647. https://doi.org/10.1016/j.asj.2008.09.008 Turner, T. (2019, July 30). Allergan cites rare cancer as reason for breast implant recall. Drugwatch. https://www.drugwatch.com/news/2019/07/30/allergan-cites-rare-cancer-breast-implant-recall/ Übeylı, E. D., & Güler, İ. (2004). Spectral analysis of internal carotid arterial Doppler signals using FFT, AR, MA, and Arma methods. Computers in Biology and Medicine, 34(4), 293–306. https://doi.org/10.1016/s0010-4825(03)00060-x U.S. GAO. (2011, June 14). Medical devices: FDA should enhance its oversight of recalls. U.S. Government Accountability Office. https://www.gao.gov/products/gao-11-468 Wagner, A. K., Soumerai, S. B., Zhang, F., & Ross-Degnan, D. (2002). Segmented regression analysis of interrupted time series studies in medication use research. Journal of Clinical Pharmacy and Therapeutics, 27(4), 299–309. https://doi.org/10.1046/j.1365-2710.2002.00430.x World Health Organization. (2023, September 11). Patient safety. https://www.who.int/news-room/fact-sheets/detail/patient-safety Wilson, N. A., & Drozda, J. (2013). Value of unique device identification in the digital health infrastructure. JAMA, 309(20), 2107. https://doi.org/10.1001/jama.2013.5514 Wilson, N. A., Tcheng, J. E., Graham, J., & Drozda Jr, J. P. (2021). Advancing patient safety surrounding medical devices: A health system roadmap to implement unique device identification at the point of care. Medical Devices, Volume 14, 411–421. https://doi.org/10.2147/mder.s339232 Wilson, N. A., Tcheng, J. E., Graham, J., & Drozda, J. P., Jr (2022). Advancing patient safety surrounding medical devices: Barriers, strategies, and next steps in health system implementation of unique device identifiers. Medical devices, 15, 177–186. https://doi.org/10.2147/MDER.S364539 Wood, S. F., & Perosino, K. L. (2008). Increasing transparency at the FDA: The impact of the FDA amendments act of 2007. Public Health Reports, 123(4), 527–530. https://doi.org/10.1177/003335490812300415 Zhang, F., Wagner, A. K., & Ross-Degnan, D. (2011). Simulation-based power calculation for designing interrupted time series analyses of health policy interventions. Journal of Clinical Epidemiology, 64(11), 1252–1261. https://doi.org/10.1016/j.jclinepi.2011.02.007 Zhang, W.-Q., Tang, W., Hu, F.-H., Jia, Y.-J., Ge, M.-W., Zhao, D.-Y., Shen, W.-Q., Zha, M.-L., & Chen, H.-L. (2023). Impact of the national nursing development plan on nursing human resources in China: An interrupted time series analysis for 1978–2021. International Journal of Nursing Studies, 148, 104612. https://doi.org/10.1016/j.ijnurstu.2023.104612 Zhao, Z., Zhai, M., Li, G., Gao, X., Song, W., Wang, X., Ren, H., Cui, Y., Qiao, Y., Ren, J., Chen, L., & Qiu, L. (2023). Study on the prediction effect of a combined model of SARIMA and LSTM based on SSA for influenza in Shanxi Province, China. BMC infectious diseases, 23(1), 71. https://doi.org/10.1186/s12879-023-08025-1 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98322 | - |
| dc.description.abstract | 為加強醫療器械的識別追蹤和上市後監管,美國食品和藥物管理局實施了唯一設備標識(Unique Device Identification,UDI)制度,以提升器械可追溯性和不良事件監測。然而,針對高風險植入式醫療器械的UDI政策效果尚缺乏實證評估。本研究以Mentor公司生產的MemoryGel乳房植入體(III類高風險醫療器械)為例,評估UDI實施對其不良事件報告趨勢的影響。我們提取了FDA不良事件資料庫(MAUDE)2010–2020年的報告資料,聚焦“受傷”類事件,採用中斷時間序列分析框架並結合SARIMA模型進行分析,將2014年10月設為干預時點。結果顯示,UDI實施後不良事件報告數出現顯著的立即增加:自2014年10月起,MemoryGel植入體相關“受傷”事件的月報告數躍升約116例(p<0.001)。此外,不良事件報告的長期趨勢由實施前的逐月緩升(約+0.95例/月)轉為實施後的逐月下降(約-0.85例/月),斜率淨變化約-1.80(p<0.001)。這些發現表明,UDI制度通過強化器械層級可追溯性,提高了不良事件報告的完整性和準確性。更重要的是,追溯能力的提升使監管者和製造商及時發現並糾正器械缺陷(如定向召回或技術改進),從源頭減少不良事件發生並提高患者安全。綜上,UDI政策顯著強化了高風險植入式醫療器械的可追溯性和上市後安全監測,為這一監管舉措的有效性提供了有力實證支援。 | zh_TW |
| dc.description.abstract | Medical device-associated adverse events have raised serious patient safety concerns, prompting regulatory initiatives to improve device identification and tracking. The U.S. Food and Drug Administration (FDA) implemented the Unique Device Identification (UDI) system, which assigns a unique code to each medical device to enhance traceability and post-market surveillance. However, empirical evidence on UDI’s impact on safety outcomes for high-risk implantable devices remains limited.
This study provides an empirical evaluation of the UDI policy’s effect on adverse event reporting trends for a high-risk implantable medical device: the Mentor MemoryGel breast implant. Adverse event records from January 2010 through December 2020 were extracted from the FDA’s Manufacturer and User Facility Device Experience (MAUDE) database. We focused on reported patient injuries associated with this device and employed an interrupted time series design using a seasonal autoregressive integrated moving average (SARIMA) model. The analysis considered October 2014—when UDI compliance became mandatory for Class III devices—as the intervention time point. Results indicate that UDI implementation was associated with a statistically significant immediate increase in adverse event reports. In particular, the monthly count of injury-related reports for MemoryGel breast implants jumped by approximately 116 cases immediately after UDI enforcement began (p < 0.001). Moreover, the long-term trend of adverse events reversed direction following UDI: prior to late 2014, monthly injury reports were gradually increasing (by about +0.95 per month), whereas after UDI implementation they showed a gradual decline (by about –0.85 per month). This change in slope (approximately –1.80 reports per month) was statistically significant (p < 0.001). These findings suggest that the UDI system improved the completeness and accuracy of adverse event reporting by greatly enhancing device traceability, thereby strengthening post-market monitoring and early risk detection. More importantly, improved traceability enabled manufacturers and regulators to more rapidly identify and correct device defects (e.g., through targeted recalls or technical modifications), which likely contributed to a reduced incidence of adverse events over time and improved patient safety. In conclusion, the UDI policy has had a markedly positive impact on the traceability and safety monitoring of high-risk implantable medical devices, providing strong empirical support for this regulatory initiative and its important role in protecting public health. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-01T16:12:58Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-01T16:12:58Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 I
誌謝 II 摘要 III ABSTRACT IV 目次 VI 圖次 VII 表次 VIII 第一章 前言 1 第一節 研究背景和研究動機 1 第二節 研究目的與研究問題 9 第二章 美國醫療器械標記法的歷史沿革及文獻綜述 11 第一節 美國醫療器械標記法案的歷史沿革 12 第二節 病人安全與醫療器械風險 26 第三節 MAUDE資料庫及醫療器械不良事件報告制度簡介 29 第四節 基於MAUDE資料庫的現有研究 36 第五節 UDI政策相關研究現狀 39 第六節 Mentor MemoryGel乳房植入體之臨床研究綜述與方法斷裂問題 43 第七節 小結 45 第三章 研究設計 47 第一節 研究假設 48 第二節 資料來源、時間範圍、研究目標選擇 49 第三節 中斷時間序列(ITS)方法與SARIMA建模 56 第四節 資料處理 73 第五節 SARIMA模型的構建 78 第四章 統計結果分析 87 第一節 描述性統計分析 87 第二節 ITS-SARIMA模型估計結果 90 第三節 模型診斷 94 第四節 內部效度分析 100 第五章 結論與建議 105 第一節 研究發現與政策意涵 105 第二節 研究限制與未來研究建議 112 參 考 文 獻 123 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 可追溯性 | zh_TW |
| dc.subject | 中斷時間序列分析 | zh_TW |
| dc.subject | SARIMA模型 | zh_TW |
| dc.subject | 唯一設備標識 | zh_TW |
| dc.subject | 高風險植入式醫療器械 | zh_TW |
| dc.subject | Mentor MemoryGel乳房植入體 | zh_TW |
| dc.subject | MAUDE資料庫 | zh_TW |
| dc.subject | Mentor MemoryGel breast implant | en |
| dc.subject | MAUDE database | en |
| dc.subject | Seasonal autoregressive integrated moving average (SARIMA) model | en |
| dc.subject | Interrupted time series analysis | en |
| dc.subject | Traceability | en |
| dc.subject | High-risk implantable medical device | en |
| dc.subject | Unique Device Identification | en |
| dc.title | UDI政策對高風險植入式醫療器械不良事件趨勢的影響 | zh_TW |
| dc.title | Impact of UDI Policy on the Trend of Adverse Events in High-Risk Implantable Devices | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林澤民;李達宇 | zh_TW |
| dc.contributor.oralexamcommittee | Tse-min Lin ;JOHN TAYU LEE | en |
| dc.subject.keyword | 唯一設備標識,高風險植入式醫療器械,可追溯性,中斷時間序列分析,SARIMA模型,MAUDE資料庫,Mentor MemoryGel乳房植入體, | zh_TW |
| dc.subject.keyword | Unique Device Identification,High-risk implantable medical device,Traceability,Interrupted time series analysis,Seasonal autoregressive integrated moving average (SARIMA) model,MAUDE database,Mentor MemoryGel breast implant, | en |
| dc.relation.page | 141 | - |
| dc.identifier.doi | 10.6342/NTU202502575 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-07-30 | - |
| dc.contributor.author-college | 社會科學院 | - |
| dc.contributor.author-dept | 公共事務研究所 | - |
| dc.date.embargo-lift | 2025-08-02 | - |
| 顯示於系所單位: | 公共事務研究所 | |
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