請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/69768完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 歐陽彥正(Yen-Jen Oyang) | |
| dc.contributor.author | Jui-Hung Kao | en |
| dc.contributor.author | 高瑞鴻 | zh_TW |
| dc.date.accessioned | 2021-06-17T03:27:07Z | - |
| dc.date.available | 2018-05-17 | |
| dc.date.copyright | 2018-05-17 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-04-19 | |
| dc.identifier.citation | REFERENCE
[1] Y.-J. Syue, Y.-L. Yen, S.-Y. Cheng, C.-W. Hung, K.-H. Wu, and Y.-R. Lin, 'Characteristics and Risk Factors of Out-of-Hospital Cardiac Arrest Within 72 Hours After Discharge,' The American journal of the medical sciences, vol. 350, no. 4, pp. 272-278, 2015. [2] E. Markusohn et al., 'Primary percutaneous coronary intervention after out-of-hospital cardiac arrest: patients and outcomes,' Hypertension, vol. 12, p. 48, 2007. [3] D. A. v. Hoeijen, M. T. Blom, A. Bardai, P. C. Souverein, A. d. Boer, and H. L. Tan, 'Reduced Prehospital Survival Rate After Out-of-Hospital Cardiac Arrest in Patients with Diabetes Mellitus Type 2: A Prospective Community-Based Study,' Circulation, vol. 130, pp. A246-A246, 2014. [4] H. Davies, A. Loosely, S. Dolling, and R. Eve, 'Predicting survival in patients admitted to intensive care following out-of-hospital cardiac arrest using the Prognosis After Resuscitation score,' Critical Care, vol. 18, no. Suppl 1, p. 491, 2014. [5] C. Maupain et al., 'The CAHP (Cardiac Arrest Hospital Prognosis) score: a tool for risk stratification after out-of-hospital cardiac arrest,' European heart journal, p. ehv556, 2015. [6] M. E. van Genderen, A. Lima, M. Akkerhuis, J. Bakker, and J. van Bommel, 'Persistent peripheral and microcirculatory perfusion alterations after out-of-hospital cardiac arrest are associated with poor survival*,' Critical care medicine, vol. 40, no. 8, pp. 2287-2294, 2012. [7] C. Sasson, M. A. Rogers, J. Dahl, and A. L. Kellermann, 'Predictors of survival from out-of-hospital cardiac arrest a systematic review and meta-analysis,' Circulation: Cardiovascular Quality and Outcomes, vol. 3, no. 1, pp. 63-81, 2010. [8] A. S. Go et al., 'Heart disease and stroke statistics--2014 update: a report from the American Heart Association,' Circulation, vol. 129, no. 3, p. e28, 2014. [9] G. Nichol et al., 'Regional variation in out-of-hospital cardiac arrest incidence and outcome,' Jama, vol. 300, no. 12, pp. 1423-1431, 2008. [10] B. McNally and A. L. Valderrama, 'Out-of-hospital cardiac arrest surveillance: Cardiac Arrest Registry to Enhance Survival (CARES), United States, October 1, 2005-December 31, 2010,' 2011. [11] B. Manuel, 'Will models of naturally occurring disease in animals reduce the bench-to-bedside gap in biomedical research?,' Zhonghua wei zhong bing ji jiu yi xue, vol. 25, no. 1, pp. 5-7, 2013. [12] C.-k. Lee, 'The Analysis of Ilan’s Out-of-Hospital Cardiac Arrest (OHCA) Patients,' National Sun Yat-sen University Library Executive MBA Thesis, 2010. [13] A. H. Brainard, W. Raynovich, D. Tandberg, and E. J. Bedrick, 'The prehospital 12-lead electrocardiogram's effect on time to initiation of reperfusion therapy: a systematic review and meta-analysis of existing literature,' The American journal of emergency medicine, vol. 23, no. 3, pp. 351-356, 2005. [14] M. W. Wandling, A. B. Nathens, M. B. Shapiro, and E. R. Haut, 'Police transport versus ground EMS: a trauma system-level evaluation of prehospital care policies and their effect on clinical outcomes,' Journal of Trauma and Acute Care Surgery, vol. 81, no. 5, pp. 931-935, 2016. [15] E. J. MacKenzie et al., 'A national evaluation of the effect of trauma-center care on mortality,' New England Journal of Medicine, vol. 354, no. 4, pp. 366-378, 2006. [16] M. Fujita, Y. Sato, K. Nagashima, S. Takahashi, and A. Hata, 'Impact of geographic accessibility on utilization of the annual health check-ups by income level in Japan: A multilevel analysis,' PloS one, vol. 12, no. 5, p. e0177091, 2017. [17] M. o. H. a. W. R. o. China. The cause of death statistics. Available: http://www.mohw.gov.tw/cht/DOS/Statistic.aspx?f_list_no=312 ,Access date, 2015/05/18 [18] T. D. Valenzuela, D. J. Roe, S. Cretin, D. W. Spaite, and M. P. Larsen, 'Estimating effectiveness of cardiac arrest interventions a logistic regression survival model,' Circulation, vol. 96, no. 10, pp. 3308-3313, 1997. [19] C. Sasson et al., 'Small area variations in out-of-hospital cardiac arrest: does the neighborhood matter?,' Annals of internal medicine, vol. 153, no. 1, pp. 19-22, 2010. [20] E. D. Root, L. Gonzales, D. E. Persse, P. R. Hinchey, B. McNally, and C. Sasson, 'A tale of two cities: the role of neighborhood socioeconomic status in spatial clustering of bystander CPR in Austin and Houston,' Resuscitation, vol. 84, no. 6, pp. 752-759, 2013. [21] C. Sasson et al., 'Increasing Cardiopulmonary Resuscitation Provision in Communities With Low Bystander Cardiopulmonary Resuscitation Rates A Science Advisory From the American Heart Association for Healthcare Providers, Policymakers, Public Health Departments, and Community Leaders,' Circulation, vol. 127, no. 12, pp. 1342-1350, 2013. [22] M. Kulldorff, 'A spatial scan statistic,' Communications in Statistics-Theory and methods, vol. 26, no. 6, pp. 1481-1496, 1997. [23] L. A. Waller and C. A. Gotway, Applied spatial statistics for public health data. John Wiley & Sons, 2004. [24] W. Zong-ping and Y. Chi-Yu, 'A Study on Resuscitative Outcome of Out-of-Hospital Cardiac Arrest Patients-The Implementation Experience of the Fire Bureau of Taipei County,' Journal of Crisis Management, vol. 8, no. 1, pp. pp.9 - 18, 2011. [25] M. o. H. a. Welfare. (2013). Department of Medical Affairs-Emergency Medical Services. Available: http://www.mohw.gov.tw/CHT/DOMA/DM1.aspx?f_list_no=935&fod_list_no=5772 [26] C. Ming-Tai, 'An Evaluation of Emergency Medical Dispatch System in Taipei,' Master of Public Health Degree Program College of Public Health, National Taiwan University, Master Thesis, 2011. [27] Y. Zui-Shen, 'Association between Particulate Matter and the Incidence of Out-of-Hospital Cardiac Arrest,' Institute of Epidemiology and Preventive Medicine, National Taiwan University, Doctoral Dissertation, 2012. [28] C.-k. Lee, 'The Analysis of Ilan’s Out-of-Hospital Cardiac Arrest (OHCA) Patients,' Executive Master of Business Administration Master Thesis, National Sun Yat-sen University, 2010. [29] C. Tzu-yen, 'The analysis of delayed-action response interval in the fire branch of Taoyuan County - by using RCA,' Department of Civil Engineering, National Central University,Master Thesis, 2011. [30] Y. Ting-Hsuan, 'The outcomes of CPR and related factors among non-traumatic patients with in-hospital cardial arrest in a crowded emergency department,' Chang Gung University of Nursing, Master Thesis, 2011. [31] H. Chien-Hua, 'Cardiopulmonary Resuscitation and Myocardial Dysfunction: Epidemiology, Diagnosis and Management,' College of Medicine Graduate Institute of Clinical Medicine, National Taiwan University ,Master Thesis, 2009. [32] A. Shin-ichi et al., 'Incidence and Mortality of Acute Myocardial Infarction A Population-Based Study Including Patients With Out-of-Hospital Cardiac Arrest,' International heart journal, vol. 52, no. 4, pp. 197-202, 2011. [33] K. Chia-Te, 'Pre-arrest factors influencing survival after in-hospital cardiopulmonary resuscitation on the general wards,' Department of Public Health, Kaohsiung Medica Universty , Master Thesis, 2011. [34] G. Kawamura et al., 'Causes of out of Hospital Cardiac Arrest without the Organic Heart Disease ' Journal of the American College of Cardiology, vol. 67, no. 13_S, pp. 321-321, 2016. [35] A. Broderick, J. Williams, A. Maryashina, D. Juthani, M. Wu, and K. James, 'Review of In-Hospital and Out-of-Hospital Cardiac Arrests at a Tertiary Community Hospital for Potential ECPR Rescue,' 2015. [36] Z. Nehme et al., 'Effect of diabetes and pre-hospital blood glucose level on survival and recovery after out-of-hospital cardiac arrest,' Critical care and resuscitation: journal of the Australasian Academy of Critical Care Medicine, vol. 18, no. 2, pp. 77-77, 2016. [37] R. O. Cummins et al., 'Recommended guidelines for reviewing, reporting, and conducting research on in-hospital resuscitation: the in-hospital “Utstein style”,' Annals of emergency medicine, vol. 29, no. 5, pp. 650-679, 1997. [38] S. C. Danciu, L. Klein, M. M. Hosseini, L. Ibrahim, B. W. Coyle, and R. F. Kehoe, 'A predictive model for survival after in-hospital cardiopulmonary arrest,' Resuscitation, vol. 62, no. 1, pp. 35-42, 2004. [39] A. R. Council and D. A. Chamberlain, 'Recommended guidelines for uniform reporting of data from out-of-hospital cardiac arrest: the Utstein style,' Annals of emergency medicine, vol. 20, no. 8, pp. 861-874, 1991. [40] C. Y. Y. Z.P.Wu, 'A Study on Resuscitative Outcome of Out-of-Hospital Cardiac Arrest Patients-The Implementation Experience of the Fire Bureau of Taipei County,' Journal of Crisis Management, vol. Vol. 8, no. No. 1, pp. pp. 9-18, 2011. [41] H.-L. Liu et al., 'Factors Determining the Clinical Outcome in Patients Suffering Cardiac or Non-Cardiac out-of-Hospital Cardiac Arrest, Who Have Achieved Sustained ROSC in the Emergency Department,' Journal of Emergency Medicine,, vol. Vol. 11, no. No. 3, pp. pp. 77-85, 2009. [42] A. Laish-Farkash, S. Matetzky, S. Kassem, H. Haj-Iahia, and H. Hod, 'Therapeutic hypothermia for comatose survivors after cardiac arrest,' The Israel Medical Association journal: IMAJ, vol. Vol.9, no. No. 4, pp. pp 252-256, 2007. [43] J. E. Bray, D. Stub, S. Bernard, and K. Smith, 'Exploring gender differences and the “oestrogen effect” in an Australian out-of-hospital cardiac arrest population,' Resuscitation, 2012. [44] H. Søholm et al., 'Tertiary centres have improved survival compared to other hospitals in the Copenhagen area after out-of-hospital cardiac arrest,' Resuscitation, 2012. [45] B. Schuller, F. Friedmann, and F. Eyben, 'The Munich Biovoice Corpus: Effects of physical exercising, heart rate, and skin conductance on human speech production,' in Language Resources and Evaluation Conference, 2014. [46] J. Zhou et al., 'Multivariate logistic regression analysis of postoperative complications and risk model establishment of gastrectomy for gastric cancer: A single-center cohort report,' Scandinavian journal of gastroenterology, vol. 51, no. 1, pp. 8-15, 2015. [47] L. Anselin, 'Local indicators of spatial association—LISA,' Geographical analysis, vol. 27, no. 2, pp. 93-115, 1995. [48] A. Getis and J. K. Ord, 'The analysis of spatial association by use of distance statistics,' Geographical analysis, vol. 24, no. 3, pp. 189-206, 1992. [49] A. F. Nassel et al., 'Multiple cluster analysis for the identification of high-risk census tracts for out-of-hospital cardiac arrest (OHCA) in Denver, Colorado,' Resuscitation, vol. 85, no. 12, pp. 1667-1673, 2014. [50] B. W. Silverman, Density estimation for statistics and data analysis. CRC press, 1986. [51] A. P. Ker and B. K. Goodwin, 'Nonparametric estimation of crop insurance rates revisited,' American Journal of Agricultural Economics, vol. 82, no. 2, pp. 463-478, 2000. [52] Y.-J. Oyang, S.-C. Hwang, Y.-Y. Ou, C.-Y. Chen, and Z.-W. Chen, 'Data classification with radial basis function networks based on a novel kernel density estimation algorithm,' IEEE transactions on neural networks, vol. 16, no. 1, pp. 225-236, 2005. [53] Y.-J. Oyang, Y.-Y. Ou, S.-C. Hwang, C.-Y. Chen, and D. T.-H. Chang, 'Data classification with a relaxed model of variable kernel density estimation,' in Neural Networks, 2005. IJCNN'05. Proceedings. 2005 IEEE International Joint Conference on, 2005, vol. 5, pp. 2831-2836: IEEE. [54] L. Anselin, Spatial econometrics: methods and models. Springer Science & Business Media, 2013. [55] S. Kanuganti, A. Sarkar, and A. P. Singh, 'Quantifying Accessibility to Health Care Using Two-step Floating Catchment Area Method (2SFCA): A Case Study in Rajasthan,' Transportation Research Procedia, vol. 17, pp. 391-399, 2016. [56] W. Luo and F. Wang, 'Measures of spatial accessibility to health care in a GIS environment: synthesis and a case study in the Chicago region,' Environment and Planning B: Planning and Design, vol. 30, no. 6, pp. 865-884, 2003. [57] A. S. Fotheringham, C. Brunsdon, and M. Charlton, Geographically weighted regression: the analysis of spatially varying relationships. John Wiley & Sons, 2003. [58] A. S. Fotheringham, M. E. Charlton, and C. Brunsdon, 'Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis,' Environment and planning A, vol. 30, no. 11, pp. 1905-1927, 1998. [59] H. J. Lee, Y. J. Ju, and E.-C. Park, 'Positive correlation between regional emergency medical resources and mortality in severely injured patients: results from the Korean National Hospital Discharge In-depth Survey,' Canadian Journal of Emergency Medicine, pp. 1-9, 2016. [60] D. Schechtman, J. C. He, B. M. Zosa, D. Allen, and J. A. Claridge, 'Trauma system regionalization improves mortality in patients requiring trauma laparotomy,' Journal of Trauma and Acute Care Surgery, vol. 82, no. 1, pp. 58-64, 2017. [61] G. Tansley et al., 'Development of a model to quantify the accessibility of a Canadian trauma system,' Canadian journal of emergency medicine, pp. 1-8, 2017. [62] M. F. Hazinski and J. M. Field, '2010 American Heart Association guidelines for cardiopulmonary resuscitation and emergency cardiovascular care science,' Circulation, vol. 122, no. Suppl, pp. S639-S946, 2010. [63] J. Shieh et al., 'Big data analysis of emergency medical service applied to determine the survival rate effective factors and predict the ambulance time variables,' 2017. [64] M. Swalehe and S. G. Aktas, 'Dynamic Ambulance Deployment to Reduce Ambulance Response Times using Geographic Information Systems: A Case Study of Odunpazari District of Eskisehir Province, Turkey,' Procedia Environmental Sciences, vol. 36, pp. 199-206, 2016. [65] K. Alnemer et al., 'Ambulance response time to cardiac emergencies in Riyadh,' Imam Journal of Applied Sciences, vol. 1, no. 1, p. 33, 2016. [66] A. M. Chang et al., 'Relationship between renal dysfunction and outcomes in emergency department patients with potential acute coronary syndromes,' Emerg Med J, vol. 30, no. 2, pp. 101-105, 2013. [67] L. S. Gardner et al., 'Admission glycaemia and its association with acute coronary syndrome in Emergency Department patients with chest pain,' Emerg Med J, vol. 32, no. 8, pp. 608-612, 2015. [68] K. Henry et al., 'Out-of-hospital cardiac arrest in Cork, Ireland,' Emerg Med J, pp. emermed-2011-200888, 2012. [69] L. Ogiela, 'Cognitive informatics in image semantics description, identification and automatic pattern understanding,' Neurocomputing, vol. 122, pp. 58-69, 2013. [70] M. E. H. Ong et al., 'Geographic factors are associated with increased risk for out-of hospital cardiac arrests and provision of bystander cardio-pulmonary resuscitation in Singapore,' Resuscitation, vol. 85, no. 9, pp. 1153-1160, 2014. [71] S. S. W. Lam et al., 'Dynamic ambulance reallocation for the reduction of ambulance response times using system status management,' The American journal of emergency medicine, vol. 33, no. 2, pp. 159-166, 2015. [72] J. A. Nhavoto and Å. Grönlund, 'Mobile technologies and geographic information systems to improve health care systems: a literature review,' JMIR mHealth and uHealth, vol. 2, no. 2, 2014. [73] K. Fransen, T. Neutens, P. De Maeyer, and G. Deruyter, 'A commuter-based two-step floating catchment area method for measuring spatial accessibility of daycare centers,' Health & place, vol. 32, pp. 65-73, 2015. [74] G. Sanson et al., 'Emergency medical service treated out-of-hospital cardiac arrest: Identification of weak links in the chain-of-survival through an epidemiological study,' European Journal of Cardiovascular Nursing, vol. 15, no. 5, pp. 328-336, 2016. [75] J. H. Park, Y. S. Ro, S. Do Shin, K. J. Song, K. J. Hong, and S. Y. Kong, 'Dispatcher-assisted bystander cardiopulmonary resuscitation in rural and urban areas and survival outcomes after out-of-hospital cardiac arrest,' Resuscitation, vol. 125, pp. 1-7, 2018. [76] S. Masterson, C. Teljeur, J. Cullinan, A. W. Murphy, C. Deasy, and A. Vellinga, 'The Effect of Rurality on Out‐of‐Hospital Cardiac Arrest Resuscitation Incidence: An Exploratory Study of a National Registry Utilizing a Categorical Approach,' The Journal of Rural Health, 2017. [77] A. Uber, R. C. Sadler, T. Chassee, and J. C. Reynolds, 'Bystander Cardiopulmonary Resuscitation Is Clustered and Associated With Neighborhood Socioeconomic Characteristics: A Geospatial Analysis of Kent County, Michigan,' Academic emergency medicine: official journal of the Society for Academic Emergency Medicine, vol. 24, no. 8, pp. 930-939, 2017. [78] F. L. Henriksen, P. Schorling, B. Hansen, H. Schakow, and M. L. Larsen, 'FirstAED emergency dispatch, global positioning of community first responders with distinct roles-a solution to reduce the response times and ensuring an AED to early defibrillation in the rural area Langeland,' International Journal of Networking and Virtual Organisations, vol. 16, no. 1, pp. 86-102, 2016. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/69768 | - |
| dc.description.abstract | 過去研究指出，需要急救患者發生到院前呼吸、心跳停止之狀況至抵達醫療院所過程中，所接受救護車上緊急處置與最救護資源分派之有效性為病患最主要的存活因子。
目前救護車標準分派程序為，救護指揮中心( Emergency Command Center)之緊急救護派遣員 （Emergency Medical Dispatch, EMD）依據當下有限的資訊與經驗直覺進行分派。然而，過去研究顯示到院前心跳停止(Out of Hospital Cardiac Arrest, OHCA)病病患的年齡、種族、發生時間、救護人員的救護能力和送醫過程…等，都影響病患的預後與存活率。以此思之，目前極少關於利用大數據與機器學習為基礎之智慧型即時性空間分析輔助緊急醫療資源派遣決策系統研究。 本研究計劃結合地理區域化緊急醫療資源資訊與救護車地理資訊收集資料庫，評估OHCA病患最有利之緊急醫療處置模式，提供例如:最近醫療院所、急救責任醫院（First-Aid Capabilities Hospital）等級、最適處理科別與分科、醫院床位資訊、特別分科需求…等建議選項以供緊急救護派遣員決策之參考。 此外，在緊急救護客觀策略提供上，本研究認為可透過大數據分析找出最佳化的人口數、區域發展程度，對緊急醫療資源分配，將結合RVKDE其換算空間涵蓋區對OHCA病人存活率之影響，或者透過增加EMT-P的空間配置與機動性，都可反映空間因素對於緊急醫療資源分配問題中的重要性，其研究結果可提供醫療資源分配之政策決議參考。 我們回顧新北市「救護指揮中心」於2010年至2011年收集發生到院前心跳停止之病患的收案案例，研究結果說明患者到院前所發生的各種狀況。本研究在研究方法上分為兩個研究進行，在研究一部份，首先採用國際通用「烏特斯坦格式」標準（Utstein Style)進行資料萃取，接著利用迴歸分析]瞭解這些變因與OHCA兩小存活率之間的關係並萃取OHCA建模的風險因子(Risk Factor)，找出OHCA風險因子係數，在研究二部份，將「OHCA空間風險因子」分析結果與「緊急醫療資源區域化數量」因子，形成空間衡量指標，做為測量不同地區人口需求，獲得醫療資源服務的數量，進而產生「緊急醫療資源區域化評估與設置最佳模式」之決策建議。 本研究將以OHCA病患為研究對象，同時將「Relaxed Variable Kernel Density Estimator」、「緊急醫療資源區域化評估與設置最佳模式」結合，此分析方式，將有助於「緊急救護派遣員」在OHCA病患到院前提供最佳醫療資源分配決策擬定，進而根據不同OHCA病患擬定最佳化救護派遣策略，本研究模型可提供醫療急救領域，實用的且有效率之分析工具，其分析結果將有助於救護派遣時參考依據。 | zh_TW |
| dc.description.abstract | According to previous research, the most important factor for a patient’s survival is the emergency treatment in the ambulance and the effective allocation of emergency resources when emergency patients experience out-of-hospital cardiac arrest (OHCA) before arriving at the hospital.
The current procedure for deciding which hospital a patient is sent to is followed by the emergency medical dispatch (EMD) sent by the emergency command centre, and they make decisions based on limited information and previous experience. Previous research indicated that the age and race of OHCA patients and the timing and rescue capacity of EMD in addition to the process of being taken to the hospital would impact a patient’s recovery and survival rate. However, very few researchers conducted studies on real-time spatial analysis of emergency medical resource allocation and dispatch based on big data and machine learning. This study intended to combine the regional information of emergency medical resources and the data base of geographic information for ambulances to assess the best method of medical treatment for OHCA patients, such as the closest medical centre, the level of first-aid capabilities, the proper medical department, the information for hospital beds, and the special needs of the medical department so that the EMD can make decisions based on these suggestions. To provide a strategy for an emergency ambulance service, this research proposed using big data analysis to examine the impact on the survival rate of OHCA patients based on the following features: the optimized population, level of regional development, allocation of emergency medical resources and the optimal execution time for the chain of survival and advanced cardiac life support. Additionally, through increasing the spatial allocation and flexibility of emergency medical technician-paramedic (EMT-P), it can reflect the importance of spatial factors in the challenge of allocating emergency medical resources. The results of this study can provide recommendations for a policy of medical resource allocation. We evaluated the cases of OHCA patients between 2010 and 2011 collected by the ambulance commend centre in New Taipei City, and the research results showed patients’ different medical conditions before arriving at the hospital. This study utilized two types of research methods. First, the study used the Utstein style to conduct data extraction and adopted a regression analysis to explore the relationship between these factors and on the OHCA patient’s survival rate, extracting an OHCA patient’s risk factor and calculating the coefficient of risk. Then, we utilized the method of decision trees to establish the OHCA’s risk decision model, adding risk factors with coordinates, and formed the OHCA spatial risk factors. In the second part of the research methods, we combined OHCA spatial risk factors with the regionalized quantity of emergency medical resources and spatial distribution of first-aid capability hospitals as well as the demand for medical resources and formed the spatial matrix to evaluate the demand for medical services. Finally, a policy recommendation for the optimized region and an allocation strategy for emergency medical resources were made based on the matrix. The object of this research is to study OHCA patients in this context. The research method combined a relaxed variable kernel density estimator with the optimized region and allocation for emergency medical resources so that the results can assist EMD in making the proper medical decision before arrival at the hospital. In addition, EMD can formulate the optimal strategy for ambulance dispatch. The model built in this research can provide a practical and efficient analytic tool for the medical emergency field and its results will provide reliable references for emergency dispatch. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T03:27:07Z (GMT). No. of bitstreams: 1 ntu-107-D99945014-1.pdf: 3427204 bytes, checksum: 3f4c503603ac858627cce748aad034a9 (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | CONTENTS
誌謝 i 中文摘要 ii ABSTRACT iv CONTENTS vii LIST OF FIGURES ix LIST OF TABLES xi Chapter 1 Introduction 1 1.1 The relationship between emergency medicine and chronic diseases 1 1.2 The regionalization of emergency medical services 3 1.3 The spatial case analysis for emergency medical aid 4 Chapter 2 Background 6 2.1 Patients and study area 6 2.2 Common Causes of OHCA 7 2.2.1 Heart Diseases 8 2.2.2 Hypertension 9 2.2.3 Diabetes 11 2.3 Utstein style 12 2.4 Risk Factors for OHCA Patients 13 2.5 Geographic information 15 Chapter 3 Methods 20 3.1 Data acquisition and preprocessing 20 3.2 Inferential statistic 22 3.2.1 Linear Regression Analysis 22 3.2.2 Logistic Regression Analysis 23 3.3 Spatial analysis 23 3.3.1 Moran's I 24 3.3.2 Kernel Density Estimation 26 3.3.3 Network Analyst 33 3.4 Relevancy Analysis of the Distribution of Emergency Aid Resources 35 3.4.1 Two-step floating catchment area method (2SFCA) 35 3.4.2 Geographically Weighted Regression (GWR) 38 Chapter 4 Experimental Results 41 4.1 Analysis of High-Risk Groups OHCA 41 4.2 Geospatial Analysis of High-Risk Groups OHCA 46 4.3 Location Analysis of Emergency Medical Cases 51 4.3.1 Regional Distribution for Emergency OHCA Cases 54 4.3.2 Maximum Load Analysis for Emergency Medical Resources 56 Chapter 5 Discussion 67 REFERENCE 72 | |
| dc.language.iso | en | |
| dc.subject | 資源管理 | zh_TW |
| dc.subject | 資訊系統 | zh_TW |
| dc.subject | 決策支援 | zh_TW |
| dc.subject | 緊急醫療 | zh_TW |
| dc.subject | 最佳化 | zh_TW |
| dc.subject | Emergency Medical | en |
| dc.subject | Information System | en |
| dc.subject | Decision Support | en |
| dc.subject | Optimization | en |
| dc.subject | Resource Management | en |
| dc.title | 空間分析輔助緊急醫療資源派遣決策之研究 | zh_TW |
| dc.title | The Study for Dispatch Decision of Medical Emergency Resources with Real-Time Spatial Analysis | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.coadvisor | 孫維仁(Wei-Zen Sun) | |
| dc.contributor.oralexamcommittee | 廖鴻圖(Horng-Twu Liaw),廖興中(Hsin-Chung Liao),周迺寬(Nai-Kuan Chou) | |
| dc.subject.keyword | 緊急醫療,資源管理,最佳化,決策支援,資訊系統, | zh_TW |
| dc.subject.keyword | Emergency Medical,Resource Management,Optimization,Decision Support,Information System, | en |
| dc.relation.page | 78 | |
| dc.identifier.doi | 10.6342/NTU201800739 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-04-19 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| 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-107-1.pdf 未授權公開取用 | 3.35 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。