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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳柏華 | |
| dc.contributor.author | Tsung-Yu Lu | en |
| dc.contributor.author | 呂宗諭 | zh_TW |
| dc.date.accessioned | 2021-06-16T05:16:16Z | - |
| dc.date.available | 2016-08-25 | |
| dc.date.copyright | 2014-08-25 | |
| dc.date.issued | 2014 | |
| dc.date.submitted | 2014-08-18 | |
| dc.identifier.citation | [1] Fitch, J., Response times: myths, measurement & management. Journal of Emergency Medical Services, 2005. 30(9): p. 47-56.
[2] Lerner, E., et al., Use of a Geographic Information System to Determine Appropriate Means of Trauma Patient Transport. Academic Emergency Medicine, 1999. 6(11): p. 1127-1133. [3] Peleg, K. and J.S. Pliskin, A geographic information system simulation model of EMS: reducing ambulance response time. The American Journal of Emergency Medicine, 2004. 22(3): p. 164-170. [4] Peters, J. and G.B. Hall, Assessment of ambulance response performance using a geographic information system. Social Science & Medicine, 1999. 49(11): p. 1551-1566. [5] Henderson, S.G. and A.J. Mason, Ambulance Service Planning: Simulation and Data Visualisation. International Series in Operations Research & Management Science, 2004. 70: p. 77-102. [6] Geroliminis, N., M.G. Karlaftis, and A. Skabardonis, A spatial queuing model for the emergency vehicle districting and location problem. Transportation Research Part B: Methodological, 2009. 43(7): p. 798-811. [7] Lim, C.S., R. Mamat, and T. Braunl, Impact of ambulance dispatch policies on performance of emergency medical services. IEEE Transactions on Intelligent Transportation Systems, 2011. 12(2): p. 624-632. [8] Rajagopalan, H.K., Ambulance Deployment and Shift Scheduling: An Integrated Approach. Journal of Service Science and Management, 2011. 04(01): p. 66-78. [9] Warden, C.R., M. Daya, and L.A. LeGrady, Using Geographic Information Systems to Evaluate Cardiac Arrest Survival. Prehospital Emergency Care, 2007. 11: p. 19-24. [10] Liu, H.-H., et al., Physical Infrastructure Assessment for Emergency Medical Response. ASCE, Journal of Computing in Civil Engineering, 2014. [11] Bailey, P.E., et al., Using a GIS to model interventions to strengthen the emergency referral system for maternal and newborn health in Ethiopia. Int J Gynaecol Obstet, 2011. 115(3): p. 300-9. [12] Ong, M.E., et al., An observational study describing the geographic-time distribution of cardiac arrests in Singapore: what is the utility of geographic information systems for planning public access defibrillation? (PADS Phase I). Resuscitation, 2008. 76(3): p. 388-396. [13] Ko, C.I., Spatial Distribution and Influence Factors of Cross-District Transports among major trauma in emergency medical services system. 2008, National Taiwan University. [14] Liu, H.-H. and A.Y. Chen, Emergency Medical Dispatch: A Case Study of New Taipei City, in 12th International Conference on Construction Application of Virtual Reality. 2012: Taipei, Taiwan. [15] Trudeau, P., et al., An operations research approach for the planning and operation of an ambulance service. Information Systems and Operational Research, 1989. 27(1): p. 95-113. [16] Channouf, N., et al., The application of forecasting techniques to modeling emergency medical system calls in Calgary, Alberta. Health Care Management Science, 2007. 10(1): p. 25-45. [17] Brown, L.H., et al., Are EMS call volume predictions based on demand pattern analysis accurate? Prehosp Emerg Care, 2007. 11(2): p. 199-203. [18] Wong, H.T. and P.C. Lai, Weather factors in the short-term forecasting of daily ambulance calls. Int J Biometeorol, 2013. [19] Zhang, G., B.E. Patuwo, and M.Y. Hu, Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 1998. 14: p. 35-62. [20] Smith, K.A. and J.N.D. Gupta, Neural networks in business: techniques and applications for the operations researcher. Computers & Operations Research, 2000. 27(11-12): p. 1023-1044. [21] Setzler, H., C. Saydam, and S. Park, EMS call volume predictions: A comparative study. Computers & Operations Research, 2009. 36(6): p. 1843-1851. [22] Nasiri, J.A., et al., ECG Arrhythmia Classification with Support Vector Machines and Genetic Algorithm, in Computer Modeling and Simulation. 2009. p. 187-192. [23] Chiu, C.-C., S.-J. Yeh, and T.-Y. Li, Classification of Diabetics with Various Degrees of Autonomic Neuropathy Based on Linear and Nonlinear Features Using Support Vector Machine, in Medical Biometrics. 2010. p. 42-51. [24] Mehta, S.S. and N.S. Lingayat, SVM-based algorithm for recognition of QRS complexes in electrocardiogram. Irbm, 2008. 29(5): p. 310-317. [25] Bishop, C.M., Pattern Recognition and Machine Learning. 2006: Springer. [26] Basak, D., S. Pal, and D.C. Patranabis, Support Vector Regression. Neural Information Processing-Letters and Reviews, 2007. 11. [27] Vapnik, V.N., The nature of statistical learning theory. 1995: Springer [28] Scholkopf, B., et al. Shrinking the tube: a new support vector regression algorithm. in Advances in Neural Information Processing Systems 11. 1999. Cambridge, MA, USA. [29] Chang, C.-C. and C.-J. Lin, LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2011. [30] Sum of Sines Models. no date [cited 2014 June 3]; Available from: http://www.mathworks.com/help/curvefit/sum-of-sine.html. [31] Gopi, G., et al. Bayesian Support Vector Regression for Traffic Speed Prediction with Error Bars. in IEEE on ITSC. 2013. [32] Lewis, C.D., INDUSTRIAL AND BUSINESS FORECASTING METHODS. 1982: LONDON (U. A.) : BUTTERWORTH SCIENTIFIC. [33] Daily Rainfall Data. No Date [cited 2014 June 3]; Available from: http://www.cwb.gov.tw/V7/climate/dailyPrecipitation/dP.htm. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/56127 | - |
| dc.description.abstract | 將具緊急救護需求的病人，及時的送達適切的醫院，是緊急醫療救護服務運作的重要目標，若能增進運作效率，則可減低重創傷病患 (Major Trauma) 的死亡數，並提升傷病患的存活率。本研究應用支持向量機(Support Vector Machine)，類神經網路(Artificial Neural Network)，迴歸分析(Regression)及移動平均法(Moving Average)，建立緊急救護需求量之預測模式。預測結果可作為決策者派遣救護車或佈署醫療資源之參考依據，並期望增進到院前之緊急醫療服務效率。本研究以人口分布、醫療資源相對不均的新北市為研究案例，採用三年之緊急救護資料，進行每三小時與每日之需求量預測。為了更加了解緊急救護案件的時空間特性，本研究引入地理資訊系統 (Geographic Information System)，幫助建構資料管理模組，並透過彈性設定欲分析之時間與空間大小，對資料的時空間特性做分類與統計，並提升本研究應用於其他地區的可能性。最後，透過本研究所建構之資料管理與預測模組，初步成果顯示在本研究所建議之架構下，能提升預測績效，並有其潛力應用於實務中。 | zh_TW |
| dc.description.abstract | The objective for Emergency Medical Services (EMSs) is to deliver patients at the right place and time with the shortest response time. By increasing the operational efficiency, the survival rate of patients could potentially be increased. The Geographic Information System (GIS) is introduced to manage and visualize the spatial distribution of training data and forecasting results. A flexible model is implemented in GIS, through which training data are prepared with user desired sizes for spatial grid and temporal steps. The authors applied Moving Average, Artificial Neural Network, Regression, and Support Vector Regression for the forecasting of pre-hospital emergency medical demand. The results from these approaches, as a reference, could be used for the pre-allocation of ambulances. A case study is conducted for the EMS in New Taipei City, where pre-hospital EMS data has been collected for 3 years. With the easy use of the model and acceptable prediction performance, the proposed approach has been shown to have its potential to be applied to the current practice. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T05:16:16Z (GMT). No. of bitstreams: 1 ntu-103-R01521529-1.pdf: 4269898 bytes, checksum: 7c1a003e7c823bad78d2f75254fcf4a5 (MD5) Previous issue date: 2014 | en |
| dc.description.tableofcontents | 摘要 i
ABSTRACT ii CHAPTER 1 INTRODUCTION 1 1.1 Background 1 1.2 Research Objectives 3 1.3 Research Flowchart and Thesis Organization 4 CHAPTER 2 LITERATURE REVIEW 6 2.1 Deployment of Ambulance 6 2.2 Distribution of Medical Resource 7 2.3 Demand Forecast in EMS 8 2.4 Introduction to SVR 11 2.5 Summary 12 CHAPTER 3 METHODOLOGY 13 3.1 Flowchart of Methodology 13 3.2 Machine Learning Method using in This Study 15 3.2.1 Support Vector Regression 15 3.2.2 Artificial Neural Network 18 3.3 Input Feature 20 3.4 Regression and Moving Average 22 3.5 Models selection and Batch Training 23 3.6 Error Indictors 25 3.7 Summary 26 CHAPTER 4 CASE STUDY AND DISCUSSION 27 4.1 Background in New Taipei City 27 4.2 Case in Banqiao District 29 4.2.1 Sensitivity Test of Six Input Cases 29 4.2.2 Daily Forecast 33 4.3 Case in Tamsui and Yingge District 35 4.4 Analysis Time 35 4.5 Spatial Distribution of Error 37 4.6 Research Limitations 39 4.7 Summary 39 CHAPTER 5 CONCLUSIONS 41 5.1 Conclusion 41 5.2 Future Work 43 REFERENCE 44 APPENDIX 47 | |
| dc.language.iso | en | |
| dc.subject | 需求預測 | zh_TW |
| dc.subject | 類神經網路 | zh_TW |
| dc.subject | 支持向量機 | zh_TW |
| dc.subject | 地理資訊系統 | zh_TW |
| dc.subject | 緊急救護服務 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | Support Vector Machine | en |
| dc.subject | Demand Forecast | en |
| dc.subject | Emergency Medical Services | en |
| dc.subject | Artificial Neural Network | en |
| dc.subject | Geographic Information System | en |
| dc.title | 應用空間機器學習之緊急救護案件需求量預測分析 | zh_TW |
| dc.title | A GIS-based Demand Forecast using Machine Learning for Emergency Medical Services | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 102-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張堂賢,陳俊杉,游景雲,賴勇成 | |
| dc.subject.keyword | 緊急救護服務,需求預測,機器學習,類神經網路,支持向量機,地理資訊系統, | zh_TW |
| dc.subject.keyword | Demand Forecast,Emergency Medical Services,Artificial Neural Network,Geographic Information System,Support Vector Machine, | en |
| dc.relation.page | 51 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2014-08-18 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
| 顯示於系所單位： | 土木工程學系 | |
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| ntu-103-1.pdf 未授權公開取用 | 4.17 MB | Adobe PDF |
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