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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳靜枝 | |
| dc.contributor.author | Pin-Syuan Ho | en |
| dc.contributor.author | 何品璇 | zh_TW |
| dc.date.accessioned | 2021-06-08T03:04:24Z | - |
| dc.date.copyright | 2017-07-20 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-07-11 | |
| dc.identifier.citation | [1] 沈能元，4類病患看診 不必擠醫院，蘋果日報。取自http://www.appledaily.com.tw/appledaily/article/headline/20170621/37690395。2017年6月21日。
[2] 財團法人台灣網路資訊中心，2016年台灣寬頻網路使用調查報告，2016。 [3] 國家衛生研究院，全民健康保險研究資料庫，http://nhird.nhri.org.tw/en/index.htm。 [4] 衛生福利部，101年度全民健康保險醫療統計年報，2014。 [5] 衛生福利部，105年「全民健康保險醫療資源缺乏地區條件」施行區域，2015。 [6] 衛生福利部，105年度偏遠地區（含山地、離島）在地照顧服務人員教育訓練簡章，2016。 [7] 衛生福利部，2012衛生福利年報，2012。 [8] 衛生福利部，2014衛生福利統計動向，2015。 [9] 衛生福利部，2025衛生福利政策白皮書，2016。 [10] 衛生福利部，全民健康保險投保金額分級表，2012。 [11] 衛生福利部，醫院以病人為中心門診整合照護試辦計畫，2014。 [12] 《醫師法》第十一條，2012。 [13] Albert, S. M., G. J. Shevchik, S. Paone, and G. D. Martich, 'Internet-based medical visit and diagnosis for common medical problems: Experience of first user cohort,' Telemedicine and e-Health, Vol. 17, No. 4, pp. 304-308, 2011. [14] Andreassen, H. K., M. M. Bujnowska-Fedak, C. E. Chronaki, R. C. Dumitru, I. Pudule, S. Santana, H. Voss, and R. Wynn, 'European citizens' use of E-health services: A study of seven countries,' BMC Public Health, Vol. 7, No. 53, 2007. [15] Baker, L., T. H. Wagner, S. Singer, and M. K. Bundorf, 'Use of the Internet and e-mail for health care information: Results from a national survey,' JAMA, Vol. 289, No. 18, pp. 2400-2406, 2003. [16] Beck, M., 'Virtual doctor visits: What's treated and what's the cost?,' Wall Street Journal (Online), May 8, 2014. [Online]. [Accessed October 20, 2016] [17] Breiman, L., 'Random forests,' Machine Learning, Vol. 45, No. 1, pp. 5-32, 2001. [18] Breiman, L., J. H. Friedman, R. Olshen, and C. J. Stone, 'Classification and regression trees,' CRC Press, 1984. [19] Car, J. and A. Sheikh, 'Email consultations in health care: 1—scope and effectiveness,' BMJ, Vol. 329, No. 7463, pp. 435-438, 2004. [20] Car, J. and A. Sheikh, 'Telephone consultations,' BMJ, Vol. 326, pp. 966-969, 2003. [21] Cortes, C. and V. Vapnik, 'Support-vector networks,' Machine Learning, Vol. 20, No. 3, pp. 273-297, 1995. [22] Cox, D. R., 'The regression analysis of binary sequences,' Journal of the Royal Statistical Society Series B (Methodological), Vol. 20, No. 2, pp. 215-242, 1958. [23] Daniel, H. and L. S. Sulmasy, 'Policy recommendations to guide the use of telemedicine in primary care settings: An American College of Physicians position paper,' Annals of Internal Medicine, Vol. 163, No. 10, pp. 787-789, 2015. [24] Dixon, R. F. and J. E. Stahl, 'Virtual visits in a general medicine practice: A pilot study,' Telemedicine and e-Health, Vol. 14, No. 6, pp. 525-530, 2008. [25] Gardner, M. R., S. M. Jenkins, D. A. O’Neil, D. L. Wood, B. R. Spurrier, and a. S. Pruthi, 'Perceptions of video-based appointments from the patient's home: A patient survey,' Telemedicine and e-Health, Vol. 21, No. 4, pp. 281-285, 2015. [26] Gilman, M. and J. Stensland, 'Telehealth and Medicare: Payment policy, current use, and prospects for growth,' Medicare & Medicaid Research Review, Vol. 3, No. 4, 2013. [27] Han, J. and M. Kamber, Data Mining: Concepts and Techniques. Second ed. CA: Elsevier, 2006. [28] Heckerman, D., 'A tutorial on learning with Bayesian networks,' Technical Report MSR-TR-95-06, Microsoft Research, 1995. [29] Hickson, R., J. Talbert, W. C. Thornbury, N. R. Perin, and A. J. Goodin, 'Online medical care: The current state of “eVisits” in acute primary care delivery,' Telemedicine and e-Health, Vol. 21, No. 2, pp. 90-96, 2015. [30] Hiratsuka, V., R. Delafield, H. Starks, A. J. Ambrose, and M. M. Mau, 'Patient and provider perspectives on using telemedicine for chronic disease management among Native Hawaiian and Alaska Native people,' International Journal of Circumpolar Health, Vol. 72, 2013. [31] Hothorn, T., K. Hornik, and A. Zeileis, 'Unbiased recursive partitioning: A conditional inference framework,' Journal of Computational and Graphical statistics, Vol. 15, No. 3, pp. 651-674, 2006. [32] Hothorn, T. and A. Zeileis, 'partykit: A Modular Toolkit for Recursive Partytioning in R,' Journal of Machine Learning Research, Vol. 16, pp. 3905-3909, 2015. [33] Husebø, A. M. L. and M. Storm, 'Virtual visits in home health care for older adults,' The Scientific World Journal, Vol. 2014, 2014. [34] Jung, C., R. Padman, G. Shevchik, and S. Paone, 'Who are portal users vs. early e-Visit adopters? A preliminary analysis,' AMIA Annual Symposium Proceedings, Vol. 2011, pp. 1070-1079, 2011. [35] Kaufman, D. R., J. Pevzner, M. Rodriguez, J. J. Cimino, S. Ebner, L. Fields, V. Moreno, C. McGuiness, R. S. Weinstock, S. Shea, and J. Starren, 'Understanding workflow in telehealth video visits: Observations from the IDEATel project,' Journal of Biomedical Informatics, Vol. 42, No. 4, pp. 581-592, 2009. [36] Kotsiantis, S. B., 'Supervised machine learning: A review of classification techniques,' Informatica, Vol. 13, pp. 249-268, 2007. [37] Kuhn, M., S. Weston, N. Coulter, and M. Culp, 'C50: C5.0 Decision Trees and Rule-Based Models,' C code for C5.0 by R. Quinlan, https://CRAN.R-project.org/package=C50, 2015. [38] LeRouge, C., M. J. Garfield, and A. R. Hevner, 'Quality attributes in telemedicine video conferencing,' Proceedings of the 35th Annual Hawaii International Conference on System Sciences, Hawaii, USA, pp. 2050-2059, 2002. [39] Liaw, A. and M. Wiener, 'Classification and Regression by randomForest,' R News, Vol. 2, No. 3, pp. 18-22, 2002. [40] Liederman, E. M., J. C. Lee, V. H. Baquero, and P. G. Seites, 'Patient-physician web messaging,' Journal of General Internal Medicine, Vol. 20, No. 1, pp. 52-57, 2005. [41] Liederman, E. M. and C. S. Morefield, 'Web messaging: A new tool for patient-physician communication,' Journal of the American Medical Informatics Association, Vol. 10, No. 3, pp. 260-270, 2003. [42] Lin, C.-T., L. Wittevrongel, L. Moore, B. L. Beaty, and S. E. Ross, 'An Internet-based patient-provider communication system: Randomized controlled trial,' Journal of Medical Internet Research, Vol. 7, No. 4, 2005. [43] Lin, M.-H., A.-C. Yang, and T.-H. Wen, 'Using regional differences and demographic characteristics to evaluate the principles of estimation of the residence of the population in National Health Insurance Research Databases (NHIRD),' Vol. 30, No. 4, pp. 347-361, 2011. [44] McClelland, J. L., D. E. Rumelhart, and G. E. Hinton, 'The appeal of parallel distributed processing,' MIT Press, pp. 3-44, 1986. [45] Mehrotra, A., S. Paone, G. D. Martich, S. M. Albert, and G. J. Shevchik, 'Characteristics of patients who seek care via eVisits instead of office visits,' Telemedicine and e-Health, Vol. 19, No. 7, pp. 515-519, 2013. [46] Nijland, N., J. E. W. C. v. Gemert-Pijnen, H. Boer, M. F. Steehouder, and E. R. Seydel, 'Increasing the use of e-consultation in primary care: Results of an online survey among non-users of e-consultation,' International Journal of Medical Informatics, Vol. 78, No. 10, pp. 688-703, 2009. [47] O’Gorman, L. D., J. C. Hogenbirk, and W. Warry, 'Clinical telemedicine utilization in Ontario over the Ontario Telemedicine Network,' Telemedicine and e-Health, Vol. 22, No. 6, pp. 473-479, 2016. [48] Pearl, J., 'Probabilistic reasoning in intelligent systems: Networks of plausible inference,' Morgan Kaufmann, 1988. [49] Polinski, J. M., T. Barker, N. Gagliano, A. Sussman, T. A. Brennan, and W. H. Shrank, 'Patients’ satisfaction with and preference for telehealth visits,' Journal of General Internal Medicine, Vol. 31, No. 3, pp. 269-275, 2016. [50] Quinlan, J. R., 'C4. 5: Programming for machine learning. ,' Morgan Kauffmann, Vol. 38, 1993. [51] Quinlan, J. R., 'Induction of decision trees,' Machine Learning, Vol. 1, No. 1, pp. 81-106, 1986. [52] R Core Team, 'R: A language and environment for statistical computing,' R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. 2016. [53] Rohrer, J. E., K. B. Angstman, S. C. Adamson, M. E. Bernard, J. W. Bachman, and M. E. Morgan, 'Impact of online primary care visits on standard costs: A pilot study,' Population Health Management, Vol. 13, No. 2, pp. 59-63, 2010. [54] Russo, J. E., R. R. McCool, and L. Davies, 'VA telemedicine: An analysis of cost and time savings,' Telemedicine and e-Health, Vol. 22, No. 3, pp. 209-215, 2016. [55] Smith, A. C., K. Youngberry, F. Christie, A. Isles, R. McCrossin, M. Williams, J. V. d. Westhuyzen, and R. Wootton, 'The family costs of attending hospital outpatient appointments via videoconference and in person,' Journal of Telemedicine and Telecare, Vol. 9, No. suppl 2, pp. 58-61, 2003. [56] Spooner, S. A. and E. M. Gotlieb, 'Telemedicine: Pediatric applications,' Pediatrics, Vol. 113, No. 6, 2004. [57] Stahl, J. E. and R. F. Dixon, 'Acceptability and willingness to pay for primary care videoconferencing: A randomized controlled trial,' Journal of Telemedicine and Telecare, Vol. 16, No. 3, pp. 147-151, 2010. [58] Summerfelt, W. T., S. Sulo, A. Robinson, D. Chess, and K. Catanzano, 'Scalable hospital at home with virtual physician visits: Pilot study,' The American Journal of Managed Care, Vol. 21, No. 10, pp. 675-684, 2015. [59] Thaker, D. A., R. Monypenny, I. Olver, and S. Sabesan, 'Cost savings from a telemedicine model of care in northern Queensland, Australia,' Medical Journal of Australia, Vol. 199, No. 6, pp. 414-417, 2013. [60] Therneau, T., B. Atkinson, and B. Ripley, 'rpart: Recursive Partitioning and Regression Trees,' https://CRAN.R-project.org/package=rpart, 2015. [61] Uusitalo, L., 'Advantages and challenges of Bayesian networks in environmental modelling,' Ecological Modelling, Vol. 203, No. 3, pp. 312-318, 2007. [62] Venkataraman, V., S. J. Donohue, K. M. Biglan, P. Wicks, and E. R. Dorsey, 'Virtual visits for Parkinson disease: A case series,' Neurology: Clinical Practice, Vol. 4, No. 2, pp. 146-152, 2014. [63] Verhoeven, F., L. v. Gemert-Pijnen, K. Dijkstra, N. Nijland, E. Seydel, and M. Steehouder, 'The contribution of teleconsultation and videoconferencing to diabetes care: A systematic literature review,' Journal of Medical Internet Research, Vol. 9, No. 5, 2007. [64] Viers, B. R., D. J. Lightner, M. E. Rivera, M. K. Tollefson, S. A. Boorjian, R. J. Karnes, R. H. Thompson, D. A. O’Neil, R. L. Hamilton, M. R. Gardner, M. Bundrick, S. M. Jenkins, S. Pruthi, I. Frank, and M. T. Gettman, 'Efficiency, satisfaction, and costs for remote video visits following radical prostatectomy: A randomized controlled trial,' European Urology, Vol. 68, No. 4, pp. 729-735, 2015. [65] Wade, V. A., J. Karnon, A. G. Elshaug, and J. E. Hiller, 'A systematic review of economic analyses of telehealth services using real time video communication,' BMC Health Services Research, Vol. 10, No. 1, 2010. [66] Wasson, J., C. Gaudette, F. Whaley, A. Sauvigne, P. Baribeau, and H. G. Welch, 'Telephone care as a substitute for routine clinic follow-up,' JAMA, Vol. 267, No. 13, pp. 1788-1793, 1992. [67] Whitten, P., B.Holtz, and C. LaPlante, 'Telemedicine: What have we learned,' Applied Clinical Informatics, Vol. 1, No. 2, pp. 132-141, 2010. [68] Whitten, P., L. Buis, and B. Love, 'Physician-patient e-visit programs: Implementation and appropriateness,' Disease Management & Health Outcomes, Vol. 15, No. 4, pp. 207-214, 2007. [69] Zhou, Y. Y., T. Garrido, H. L. Chin, A. M. Wiesenthal, and L. L. Liang, 'Patient access to an electronic health record with secure messaging: Impact on primary care utilization,' American Journal of Managed Care, Vol. 13, No. 7, pp. 418-424, 2007. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/20804 | - |
| dc.description.abstract | 線上看診透過網路平台使病人可在線上取得醫療看診服務，相較於一般門診的看診方式，線上看診具有更高的效率以及便利性。尤其對於居住在偏鄉地區、行動不便、患有慢性疾病的病人來說，線上看診是更方便的醫療管道。目前已有許多歐美國家開放線上看診等相關醫療服務，而對於尚未開放線上看診的國家，應在開放線上看診或擬定相關政策前，對於開放線上看診所帶來的影響進行全面的預估以及規劃。
本研究提出一以決策樹為基礎的線上看診分類方法分類病人會使用線上看診的情況，我們藉由現有的線上看診研究所提供會使用線上看診的病人特徵，以及病人的看診記錄進行分類。透過決策樹模型得到一組線上看診的分類規則。 本研究以尚未開放線上看診的台灣為例，利用台灣健保資料庫中2012年的資料進行分析，透過線上看診的決策樹分類模型得到一組線上看診的分類規則，了解病人會使用線上看診的情況，並可預估可能使用線上看診的人數比例。 本研究提出的方法利用已開放線上看診國家的研究結果分類看診記錄，並提供一組清楚的線上看診分類規則，可提供尚未開放線上看診的國家進行開放前的評估與計劃。而開放線上看診後，亦可透過此方法更新分類規則，因應病人行為模式的改變，以確保醫療服務的穩定。此方法的概念不只可應用在線上看診的預估，亦可應用在其他新服務或新產品的推行規劃。 | zh_TW |
| dc.description.abstract | E-Visit is the consultation service delivering the health care online, which is more efficient and effective than in-office visit, especially for patients living in rural area, disabled, or with chronic diseases. Some countries have already implemented e-visits. For the countries have not yet implemented e-visits, the governments have to make a comprehensive preparation and predict the effect of implementing e-visits.
This study proposes a decision-tree based e-visit classification approach (DTEVCA) to determine clinic visits qualified as e-visits using the clinics’ medical records and patients’ demographic data. This study assumes that health care insurance (i.e., national health insurance) will subsidize the e-visit service cost, in which case it is essential to identify patients who will benefit most from e-visit service. Using a large data set from Taiwan’s National Health Insurance, this study verifies the efficiency and validity of the DTEVCA. The results indicate that this approach can accurately classify clinic in-office visits that could switch to e-visit service. The straightforward rules of this decision tree also give insurance agencies a clear guideline to understand the circumstance of using e-visits and predict the effect of implementing e-visits in Taiwan. The result of this study can help the countries that have not yet implemented e-visits to improve the policy formulation process or academic researches. The DTEVCA can update the classification rules using new data to correct the biases and ensure the stability of the e-visit system. In addition, the concept of this approach is feasible not only for e-visit service but also for other “new services” such as new products or new policies. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T03:04:24Z (GMT). No. of bitstreams: 1 ntu-106-R04725001-1.pdf: 1920697 bytes, checksum: 0a73d057a77628260f7355b9811e9ade (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | Acknowledgment i
論文摘要 ii THESIS ABSTRACT iii Contents iv List of Figures vi List of Tables vii Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 Research Objectives 4 1.3 Research Scope and Limitation 5 Chapter 2 Literature Review 7 2.1 Definition of E-visits 7 2.2 Development History of E-visits 8 2.3 Advantage and Barrier of E-visits 10 2.4 Scope for E-visits 10 2.5 The Characteristics of Patients Who Use E-visits 11 2.6 Binary Classification 13 Chapter 3 Problem Description 16 3.1 Problem Description 16 3.2 Data Preparation 17 3.3 The Class Label Attribute 18 3.4 Classification 18 Chapter 4 The Decision-Tree Based E-Visit Classification Approach (DTEVCA) 20 4.1 Data Collection 21 4.2 Data Preparation 21 4.3 The Class Label Attribute 25 4.3.1 Expert Opinions 26 4.3.2 Expert Classification Model 28 4.4 E-Visit Classification Model Building 28 4.4.1 Model Training 29 4.4.2 Model Assessment 30 4.4.3 Model Selection 30 4.5 Time Complexity 31 Chapter 5 Computation Analysis 33 5.1 Data Description 34 5.2 The Classification by Experts 43 5.3 The Classification of E-Visits 49 5.4 Experiments 51 5.5 Summary 55 Chapter 6 Conclusion and Future Work 57 6.1 Conclusion 57 6.2 Future Work 58 Reference 59 Appendix A The Cross-Validation Accuracy of Three Experts’ Trees 63 Appendix B The Tree of Expert 1 64 Appendix C The Tree of Expert 2 65 Appendix D The Tree of Expert 3 67 Appendix E The Overview of the Test Datasets 68 Appendix F The Cross-Validation Accuracy of E-Visit Trees (CART) 69 Appendix G The Cross-Validation Accuracy of E-Visit Trees (C5.0) 70 Appendix H The Cross-Validation Accuracy of E-Visit Trees (Ctree) 71 Appendix I The Accuracy of 30 Test Datasets 72 Appendix J The Recall of 30 Test Datasets 73 Appendix K The Precision of 30 Test Datasets 74 Appendix L The Specificity of 30 Test Datasets 75 Appendix M The E-Visit Tree (C5.0) 76 Appendix N The Cross-Validation Accuracy of Random Forest 80 | |
| dc.language.iso | en | |
| dc.subject | 健保資料分析 | zh_TW |
| dc.subject | 線上看診 | zh_TW |
| dc.subject | 醫療數據探勘 | zh_TW |
| dc.subject | 資料分類 | zh_TW |
| dc.subject | 決策樹 | zh_TW |
| dc.subject | Data Classification | en |
| dc.subject | Healthcare Insurance Data Mining | en |
| dc.subject | Healthcare Analytics | en |
| dc.subject | Decision Tree | en |
| dc.subject | E-Visit | en |
| dc.title | 採用線上看診之決策樹分類—以台灣為例 | zh_TW |
| dc.title | The Decision-Tree Based Classification of E-Visits: A Case Study in Taiwan | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 魏志平,盧信銘,孔令傑 | |
| dc.subject.keyword | 線上看診,醫療數據探勘,資料分類,決策樹,健保資料分析, | zh_TW |
| dc.subject.keyword | E-Visit,Healthcare Analytics,Data Classification,Decision Tree,Healthcare Insurance Data Mining, | en |
| dc.relation.page | 80 | |
| dc.identifier.doi | 10.6342/NTU201701365 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2017-07-11 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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