請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/101458完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 歐陽彥正 | zh_TW |
| dc.contributor.advisor | Yen-Jen Oyang | en |
| dc.contributor.author | 高溥 | zh_TW |
| dc.contributor.author | Pu Kao | en |
| dc.date.accessioned | 2026-02-03T16:26:28Z | - |
| dc.date.available | 2026-02-04 | - |
| dc.date.copyright | 2026-02-03 | - |
| dc.date.issued | 2026 | - |
| dc.date.submitted | 2026-01-22 | - |
| dc.identifier.citation | [1] Decision tree in machine learning with example.
[2] Decision trees explained: Entropy, information gain, gini index, ccp pruning. [3] Covid-19 後疫情時代防疫政策白皮書 (white paper on taiwan's epidemic prevention policy in post-covid-19 era). Technical report, 衛生福利部疾病管制署, April 113. Published by the Taiwan CDC. [4] Understanding decision trees, 2018. [5] F. Alhaidari, A. Almuhaideb, S. Alsunaidi, et al. E-triage systems for covid-19 out- break: Review and recommendations. Sensors (Basel), 21(8):2845, 2021. Published 2021 Apr 17. [6] M. Alidadi, A. Sharifi, and D. Murakami. Tokyo’s covid-19: An urban perspective on factors influencing infection rates in a global city. Sustainable Cities and Society, 97:104743, 2023. [7] W.Babidge, D.Tivey, J.Kovoor, et al. Surgery triage during the covid-19 pandemic. ANZ Journal of Surgery, 90(9):1558–1565, 2020. [8] N. Balakrishnan, V. Voinov, and M. S. Nikulin. Chi-Squared Goodness of Fit Tests with Applications. Academic Press, 2013. Page iv. [9] Y. Chang, H. Shih, J. Wu, et al. Machine learning–based triage to identify low- severity patients with a short discharge length of stay in emergency department. BMC Emergency Medicine, 22:88, 2022. [10] Y.Chang, H.Shih, J.Wu, F.Huang, W.Chen, D.Chen, Y.Chung, and C.Wang.Ma- chine learning–based triage to identify low-severity patients with a short discharge length of stay in emergency department. BMC Emergency Medicine, 22:88, 2022. [11] S. Chatterjee, M. Bhattacharya, S. Nag, K. Dhama, and C. Chakraborty. A detailed overview of sars-cov-2 omicron: Its sub-variants, mutations and pathophysiology, clinical characteristics, immunological landscape, immune escape, and therapies. Viruses, 15(1):167, 2023. Published 2023 Jan 5. [12] W. C. Chen and A. S.-E. Huang. Covid-19 public health surveillance and response in taiwan: From containment to mitigation. Journal of the Formosan Medical Association, 123(Supplement 1):S17–S26, 2024. [13] Y.Chou, Y.Yen, D.Chu, and H.Hu. Impact of the covid19 pandemic on healthcare-seeking behaviors among frequent emergency department users: A cohort study. International Journal of Environmental Research and Public Health, 18(12):6351, 2021. Published 2021 Jun 11. [14] C.Chung. Emergency department overcrowding. HongKong Journal of Emergency Medicine, 12(3):131–132, 2005. [15] Z. Davis, C. Zobel, L. Khansa, and R. Glick. Emergency department resilience to disaster-level overcrowding: A component resilience framework for analysis and predictive modeling. Journal of Operations Management, 66:54–66, 2020. [16] T.R.Derrick and J.M.Thomas. Time series analysis: The cross-correlation function. In Innovative Analyses of Human Movement, chapter 7, pages 189–205. 2004. [17] Emergency Medicine Practice Committee. Emergency department crowding: High impact solutions. Solutions report, American College of Emergency Physicians, Irving, TX, 2016. [18] F. Fantin, S. Nicolussi Paolaz, A. Giani, E. Mattachini, E. Zoico, and M. Zamboni. Central and peripheral arterial stiffness and sarcopenia in hospitalized older adults. Aging Clinical and Experimental Research, 37(1):223, 2025. [19] C.Font-Cabrera, M.Juve-Udina, J.Adamuz, M.DiazMembrives, N.Fabrellas, and E. Guix-Comellas. Activity, triage levels and impact of the pandemic on hospital emergency departments: a multicentre cross-sectional study. Journal of Advanced Nursing, 81(3):1332–1342, 2025. [20] S. Gaeta et al. Emergency department surge and overcrowding: An interdisciplinary solution for an institutional issue. Journal of Clinical Oncology, 37:242, 2019. [21] A. Guttmann, M. J. Schull, M. J. Vermeulen, and T. A. Stukel. Association between waiting times and short term mortality and hospital admission after departure from emergency department: population based cohort study from ontario, canada. BMJ, 342:d2983, 2011. [22] J. Hinson, D. Martinez, P. Schmitz, M. Toerper, D. Radu, J. Scheulen, S. Stewart de Ramirez, and S. Levin. Accuracy of emergency department triage using the emer- gency severity index and independent predictors of under-triage and over-triage in brazil: a retrospective cohort analysis. International Journal of Emergency Medicine, 11(1):3, 2018. [23] C.-M.Hsu, L.-L.Liang, Y.-T.Chang, and W.-C.Juang. Emergency department over-crowding: Quality improvement in a taiwan medical center. Journal of the Formosan Medical Association, 118(1 Part 1):186–193, 2019. [24] T. Huabbangyang, R. Rojsaengroeng, G. Tiyawat, A. Silakoon, A. Vanichkulbodee, J. Sri-on, and S. Buathong. Associated factors of under and over-triage based on the emergency severity index: a retrospective cross-sectional study. Archives of Academic Emergency Medicine, 11(1):e57, 2023. [25] H. Kato and A. Takizawa. Time series cross-correlation between home range and number of infected people during the covid-19 pandemic in a suburban city. PLoS One, 17(9):e0267335, 2022. Published 2022 Sep 1. [26] Y. Khadem Charvadeh, G. Y. Yi, Y. Bian, and W. He. Is 14-days a sensible quarantine length for covid-19? examinations of some associated issues with a case study of covid-19 incubation times. Statistics in Biosciences, 14(1):175–190, 2022. Pub- lished correction appears in Stat Biosci. 2022;14(1):191. doi: 10.1007/s12561-021- 09322-6. [27] S. Kim, H. Kim, Y. Park, C. Kang, Y. Ro, and O. Kim. Analysis of the impact of the coronavirus disease epidemic on the emergency medical system in south korea using the korean triage and acuity scale. Yonsei Medical Journal, 62(7):631–639, 2021. [28] N. Ku, C. Tsai, S. Chen, C. Huang, C. Fang, and S. Shun. Bed-to-bed transfer pro- gram among patients who need hospitalization in a crowded emergency department in taiwan. International Journal of Health Policy and Management, 11(9):1844– 1851, 2022. [29] E. Kucewicz-Czech and M. Damps. Triage during the covid-19 pandemic. Anaesthesiology Intensive Therapy, 52(4):312–315, 2020. [30] K.-C. Leung, Y.-T. Lin, D.-Y. Hong, C.-L. Tsai, C.-H. Huang, and L.-C. Fu. A novel interpretable deep-learning-based system for triage prediction in the emergency de- partment: A prospective study. In 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 2979–2985, 2021. [31] L. Marzano, A. Darwich, R. Jayanth, et al. Diagnosing an overcrowded emergency department from its electronic health records. Scientific Reports, 14:9955, 2024. [32] B. P. McNeilly, B. J. Lawner, and T. P. Chizmar. The chronicity of emergency department crowding and rethinking the temporal boundaries of disaster medicine. Annals of Emergency Medicine, 81(3):282–285, 2023. Published 2023/03/01. [33] R. Mitchell, S. Bornstein, D. Piamnok, W. Sebby, C. Kingston, R. Tefatu, M. Kendino, B. Josaiah, J. Pole, S. Kuk, S. Körver, J.-P. Miller, T. Cole, A. Erbs, G. O’Reilly, P. Cameron, D. Sengiromo, and C. Banks. Multimodal learning for emergency department triage implementation: experiences from papua new guinea during the covid-19 pandemic. The Lancet Regional Health - Western Pacific, 33:100683, 2023. [34] C. Morley, M. Unwin, G. M. Peterson, J. Stankovich, and L. Kinsman. Emergency department crowding: A systematic review of causes, consequences and solutions. PLoS One, 13(8):e0203316, 2018. Published 2018 Aug 30. [35] D. Musalkova, L. Piherova, O. Kwasny, et al. Trends in sars-cov-2 cycle thresh- old values in the czech republic from april 2020 to april 2022. Scientific Reports, 13:6156, 2023. [36] C.Ng, Z.Yen, J.Tsai, L.Chen, S.Lin, Y.Sang, and J.Chen. Validation of the taiwan triage and acuity scale: a new computerised five-level triage system. Emergency Medicine Journal, 28(12):1026–1031, 2011. [37] C. Ng, Z. Yen, J. Tsai, et al. Validation of the taiwan triage and acuity scale: a new computerised five-level triage system. Emergency Medicine Journal, 28:1026–1031, 2011. [38] M. Pishkhani, M. Adib, M. Amiri, and M. Noveiri. Challenges and barriers of triage during coronavirus pandemic from the emergency nurses' perspective: A qualitative study. The Open Nursing Journal, 17:e187443462307260, 2023. [39] D.B.Richardson. Increase inpatient mortality at 10 days associated with emergency department overcrowding. Medical Journal of Australia, 184:213–216, 2006. [40] P. Schober, C. Boer, and L. A. Schwarte. Correlation coefficients: Appropriate use and interpretation. Anesthesia & Analgesia, 126(5):1763–1768, May 2018. [41] P. Schober and T. R. Vetter. Correlation analysis in medical research. Anesthesia & Analgesia, 130(2):332, February 2020. [42] H. Sharma and S. Kumar. A survey on decision tree algorithms of classification in data mining. International Journal of Science and Research (IJSR), 5, 04 2016. [43] R. E. Thomson and W. J. Emery. Chapter 5 - time series analysis methods. In Data Analysis Methods in Physical Oceanography (Third Edition), pages 425–591. Elsevier, 2014. [44] L. Tsai, C. Chien, C. Chen, C. Chaou, C. Ng, M. Lo, C. Seak, J. Seak, Z. Goh, and C. Seak. Impact of the coronavirus disease 2019 pandemic on an emergency department service: experience at the largest tertiary center in taiwan. Risk Management and Healthcare Policy, 14:771–777, 2021. [45] A. S. Vohra, D. Khullar, R. Kaushal, and W. L. Schpero. Many intensive care units were overloaded while nearby hospitals had excess capacity during the covid-19 pandemic. Health Affairs (Millwood), 42(7):937–945, 2023. [46] D. W. Wallace, S. L. Burleson, M. A. Heimann, J. C. Crosby, J. Swanson, C. B. Gibson, and C. Greene. An adapted emergency department triage algorithm for the covid‐19 pandemic. JACEP Open, 1(6):1374–1379, 2020. [47] Q. Wang, X. Wang, and H. Lin. The role of triage in the prevention and control of covid-19. Infection Control & Hospital Epidemiology, 41(7):772–776, 2020. [48] Y.Wang, D.Zhai, W.Xie, and S.Huang. Spatial optimization of hierarchical health- care facilities driven by multi-source data: a case study of shenyang, china. Frontiers in Public Health, 13:1640070, 2025. [49] T.Y.Wu, A.Majeed, and K.N.Kuo. An overview of the healthcare system in taiwan. London Journal of Primary Care (Abingdon), 3(2):115–119, 2010. [50] S. S. Zakariaee, N. Naderi, M. Ebrahimi, and et al. Comparing machine learning algorithms to predict covid-19 mortality using a dataset including chest computed tomography severity score data. Scientific Reports, 13:11343, 2023. [51] 謝宛婷, 林秀娟, and 林宏榮. Covid-19 大流行:匱乏醫療資源的分貝倫理、臨 終人道關懷和健康照護機構的法律責任. 醫事廣場, 63(7), 2020. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/101458 | - |
| dc.description.abstract | COVID-19 對全球造成重大衝擊。因 Omicron 變異株以輕症與無症狀傳播為主，且防疫政策調整，台灣自 2022 年 4 月下旬起本土確診數快速上升；2022 至 2023 年間共出現 4 波 Omicron 疫情，累計確診超過 1,000 萬例。大規模流行病使急診就醫量上升，進一步造成醫療資源緊繃與醫護負荷加重，並影響患者獲得適切照護。本研究以移動平均與互相關分析台灣各縣市疫情趨勢與急診檢傷人數之時間延遲關係，並以決策樹評估臨床與公衛資源變項（如床數、人口密度、累積確診數）之相對重要性。結果顯示，疫情高峰期間，急診檢傷量與人口密度呈高度正相關；人口密度較低的地區（如花蓮縣）因具備相對充足的急診醫療量能，醫療超載程度較低。本研究期望提供一套以檢傷資料為基礎之評估方法，作為公共衛生緊急事件下匱乏醫療資源之決策參考，協助醫療機構制定較公平且客觀的資源分配原則。 | zh_TW |
| dc.description.abstract | The COVID-19 pandemic has had a major global impact. In late April 2022, Taiwan experienced a rapid spread of the Omicron variant. With infections largely mild or moderate and national policy shifting from containment to mitigation, the outbreak placed substantial strain on the healthcare system. Emergency departments faced patient surges, heavier clinician workloads, and constrained medical resources. This study used moving average and cross-correlation analyses to examine lagged relationships between regional COVID-19 incidence trends and ED triage volumes. A decision tree model then evaluated the relative importance of clinical and public health resource factors. The results show a strong positive association between ED triage volume and population density during the peak period. In contrast, lower-density areas such as Hualien County, despite high cumulative case burdens, had comparatively higher bed and physician availability and showed lower levels of healthcare overload. These findings support evidence-based and equitable resource planning for urgent public health emergencies. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2026-02-03T16:26:28Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2026-02-03T16:26:28Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Verification Letter from the Oral Examination Committee i
Acknowledgements ii 摘要 iii Abstract iv Contents vi List of Figures viii List of Tables x Chapter 1 Introduction 1 1.1 Triage during the COVID-19 Pandemic 1 1.2 Aim of the Study 2 1.3 Thesis Organization 2 Chapter 2 Literature Review 4 2.1 Emergency Department Overcrowding 4 2.1.1 Disaster-level Overcrowding 5 2.2 Imbalanced Triage Acuity in Disasters 6 Chapter 3 Method 9 3.1 Data Collection and Quality Assessment 9 3.2 Data Analysis 10 3.2.1 Chi-squared Goodness-of-fit Test 10 3.2.2 Moving Averages 11 3.2.3 Correlation Coefficients 11 3.2.3.1 Pearson r 11 3.2.3.2 Spearman rho 12 3.2.3.3 Cross-correlation 13 3.2.4 Decision Tree 14 Chapter 4 Experiments 17 4.1 Chi-squared Goodness-of-fit Test 17 4.2 Moving Averages 19 4.3 Cross-correlation coefficients 22 4.4 Decision Tree Regression 24 Chapter 5 Discussion & Future Work 36 5.1 Discussion 36 5.2 Future Work 37 References 40 Appendix A — 14 Days of Moving Averages. Each County / City in Taiwan 47 A.1 Six Municipalities 47 A.2 Non-six Municipalities 51 | - |
| dc.language.iso | en | - |
| dc.subject | COVID-19 | - |
| dc.subject | 急診資源崩潰 | - |
| dc.subject | 決策樹 | - |
| dc.subject | 移動平均 | - |
| dc.subject | 互相關函數 | - |
| dc.subject | 公共衛生監測 | - |
| dc.subject | COVID-19 | - |
| dc.subject | emergency department overcrowding | - |
| dc.subject | decision tree | - |
| dc.subject | moving average | - |
| dc.subject | Cross-correlation | - |
| dc.subject | public health surveillance | - |
| dc.title | COVID-19 疫情期間臺灣急診部醫療資源壓力之研究 | zh_TW |
| dc.title | Impacts of the COVID-19 Pandemic on Surge Demands of Medical Capacity in Taiwanese Emergency Departments | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 114-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 黃乾綱;楊孟翰 | zh_TW |
| dc.contributor.oralexamcommittee | Chien-Kang Huang;Meng-Han Yang | en |
| dc.subject.keyword | COVID-19,急診資源崩潰決策樹移動平均互相關函數公共衛生監測 | zh_TW |
| dc.subject.keyword | COVID-19,emergency department overcrowdingdecision treemoving averageCross-correlationpublic health surveillance | en |
| dc.relation.page | 58 | - |
| dc.identifier.doi | 10.6342/NTU202600216 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2026-01-22 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | - |
| dc.date.embargo-lift | N/A | - |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-114-1.pdf 未授權公開取用 | 13.64 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。