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研究生： 劉德謙
De-Chian Liu
論文名稱： 基於大數據方法分析之即時智慧醫療需求量預測-個案研究
Real-time Intelligent Health Care Demand Forecasting based on Big Data Predictive Analytics - A Case Study
指導教授： 羅士哲
Shih-Che Lo
口試委員: 郭柏勳
Po-Hsun Kuo
林希偉
Shi-Woei Lin
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2019
畢業學年度： 107
語文別： 英文
論文頁數： 54
中文關鍵詞： 大數據 、時間序列分析 、預測 、類神經網路 、醫療管理
外文關鍵詞： Big Data, Time series analysis, Forecasting, Artificial neural network, Healthcare management
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    • 隨著現今科技演進，在工業 4.0 發展下，人工智慧、大數據分析及雲端運算在智慧工廠中扮演很重要的角色。本研究主要焦點放在應用大數據預測分析於醫療管理中，所謂的大數據是指具有「快速」產生「多變」的「大量」資料等特性的數據。
    在此研究，我們設計出即時智慧醫療預測系統，此系統主要分為兩大階段，第一階段為醫療需求量預測，依據歷史資料透過數個時間序列預測方法，包括加權移動平均法、指數平滑法及簡單迴歸等進行前處理，再利用 ARIMA(Autoregressive Integrated Moving Average Model)及 BPNN (Back Propagation Neural Network)預測未來醫療需求量；第二階段為提供即時病患醫療轉診策略，針對資料屬性具有心臟相關疾病的病患進行轉診預測，應用 BPNN 即時地判斷該病患需送往指定的健康中心，經由預測後的結果產生即時轉診策略，希望能提供相關個案的醫療人員醫療管理決策分析的參考。而此研究以 ABC 醫療集團的個案作為研究題材，在未來，此系統不僅侷限於此個案資料，也能應用於其他相關的資料。

    With the evolution of the information and communication technology, following the development of Industry 4.0, Artificial Intelligence (AI), Big Data, and Cloud Computing play important roles in the smart manufacturing factory. In this thesis, we focused on the big data predictive analytics, having three properties: velocity, variety and volume in healthcare management.
    In this thesis, we proposed a real time intelligent medical forecasting system, which was divided into two phases. In the first phase, a Big Data approach for Medical Demand Forecasting, including several time series forecasting methods, such as weighted moving average method, exponential smoothing method and simple linear regression, to compensate the missing values. Then, applying ARIMA and BPNN to forecast the medical demand. In the second phase called the, Real-Time Big Data Predictive Analytics for Medical Referral Strategy, we focused on the patients who contracted the cardiovascular diseases and deployed the BPNN to fit the historical data to forecast that the original health center should refer patients to the designated health center according to the type of cardiovascular diseases. Furthermore, we used the data set from the ABC medical group as a case study in the field of healthcare management and this forecasting system not only used for this case data but also it could apply to other relatively data sets.

    摘要............................................................................I ABSTRACT.......................................................................II ACKNOWLEDGEMENTS..............................................................III FIGURES........................................................................VI TABLES........................................................................VII CHAPTER 1 INTRODUCTION..........................................................1 1.1 Research Motivation.........................................................1 1.2 Objectives..................................................................3 1.3 Research Structure..........................................................3 CHAPTER 2 LITERATURE REVIEW.....................................................5 2.1 Emergency Medical Management................................................5 2.2 Forecasting Methods.........................................................6 2.3 Big Data....................................................................9 2.4 Artificial Neural Network..................................................11 CHAPTER 3 RESEARCH METHODS.....................................................14 3.1 Big Data Analytics.........................................................14 3.2 Intelligent Forecasting Model..............................................15 3.2.1 Naïve Method.............................................................18 3.2.2 Drift Method.............................................................19 3.2.3 Simple Moving Average....................................................19 3.2.4 Double Moving Average....................................................20 3.2.5 Weighted Moving Average..................................................20 3.2.6 Simple Exponential Smoothing.............................................21 3.2.7 Holt’s Exponential Smoothing.............................................21 3.2.8 Winters’ Exponential Smoothing...........................................22 3.2.9 Simple Linear Regression.................................................22 3.3 Medical Referral System Flow Chart.........................................24 3.4 Autoregressive Integrated Moving Average Model.............................25 3.5 Back Propagation Neural Network............................................26 3.6 Forecasting Performance Measures...........................................28 3.7 Real-Time Medical Referral System Framework................................29 CHAPTER 4 COMPUTATIONAL EXPERIMENTS............................................30 4.1 Case Study.................................................................30 4.2 Big Data Predictive Analytic for Medical Demand Forecasting................33 4.3 Big Data Predictive Analytic for Medical Referral Strategy.................44 CHAPTER 5 CONCLUSIONS AND FUTURE RESEARCH......................................49 5.1 Conclusions................................................................49 5.2 Future Research............................................................49 REFERENCES.....................................................................51

    Bagust, A., Place, M., & Posnett, J. W. (1999). Dynamics of bed use in accommodating emergency admissions: Stochastic simulation model. British Medical Journal, 319, 155-158.
    Bergs, J., Heerinckx, P., & Verelst, S. (2014). Knowing what to expect, forecasting monthly emergency department visits: A time-series analysis. International Emergency Nursing, 22(2), 112-115. Carvalho-Silva, M., Monteiro, M. T. T., Sá-Soares, F. d., & Dória-Nóbrega, S. (2017).Assessment of forecasting models for patients arrival at emergency department. Operations Research for Health Care, 18, 112-118.
    Chu, F.-L. (2009). Forecasting tourism demand with ARMA-based methods. Tourism
    Management, 30(5), 740-751.
    de Faria, E. L., Albuquerque, M. P., Gonzalez, J. L., Cavalcante, J. T. P., &
    Albuquerque, M. P. (2009). Predicting the Brazilian stock market through neural
    networks and adaptive exponential smoothing methods. Expert Systems with Applications, 36(10), 12506-12509.
    Demchenko, Y., Laat, C. d., & Membrey, P. (2014). Defining architecture components of the big data ecosystem. Paper presented at the 2014 International Conference on Collaboration Technologies and Systems (CTS).
    Gadepally, V., & Kepner, J. (2014). Big data dimensional analysis. Paper presented at the 2014 IEEE High Performance Extreme Computing Conference (HPEC).
    Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.
    Haiges, R., Wang, Y. D., Ghoshray, A., & Roskilly, A. P. (2017). Forecasting electricity generation capacity in Malaysia: An auto regressive integrated moving average approach. Energy Procedia, 105, 3471-3478.
    Hay, K. L., & Bull, B. S. (2009). Statistical clues to postoperative blood loss: Moving averages applied to medical data. Blood Cells, Molecules, and Diseases, 43(3), 250-255.
    Hikichi, S. E., Salgado, E. G., & Beijo, L. A. (2017). Forecasting number of ISO 14001 certifications in the Americas using ARIMA models. Journal of Cleaner
    Production, 147, 242-253.
    Ho, S. L., & Xie, M. (1998). The use of ARIMA models for reliability forecasting and analysis. Computers & Industrial Engineering, 35(1), 213-216.
    Hurwitz, J., Nugent, A., Halper, F., & Kaufman, M. (2013), Big Data for Dummies.
    Ji, H., Songlin, W., Qinglin, W., & Xiaonan, C. (2012). Douhe reservoir flood
    forecasting model based on data mining technology. Procedia Environmental
    Sciences, 12, 93-98.
    Kim, M., Zimmermann, T., DeLine, R., & Begel, A. (2016). The emerging role of data scientists on software development teams. Paper presented at the 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE).
    Laney, D. (2001). 3D data management: Controlling data volume, velocity, and
    variety. Application Delivery Strategies, File: 949 Abbendum, META Group.
    Laouafi, A., Mordjaoui, M., & Dib, D. (2014). Very short-term electricity demand
    forecasting using adaptive exponential smoothing methods. Paper presented at the 2014 15th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA).
    Larburu, N., Bults, R., van Sinderen, M., & Hermens, H. (2015). Quality-of-data
    management for telemedicine systems. Procedia Computer Science, 63, 451-458.
    Li, M., Chen, G., Lin, Z., & Cai, B. (2009). Combination forecasting model based on drift. Paper presented at the 2009 International Joint Conference on Computational Sciences and Optimization.
    Pratyaksa, H., Permanasari, A. E., Fauziati, S., & Fitriana, I. (2016). ARIMA
    implementation to predict the amount of antiseptic medicine usage in veterinary
    hospital. Paper presented at the 2016 1st International Conference on Biomedical
    Engineering (IBIOMED).
    Runkler, T. A. (2016), Data Analytics-Models and Algorithms for Intelligent Data
    Analysis.
    Seematter-Bagnoud, L., Fustinoni, S., Dung, D. H., Santos-Eggimann, B., Koehn, V., Bize, R., Oettli, A., & Wasserfallen, J. B. (2015). Comparison of different methods to forecast hospital bed needs. European Geriatric Medicine, 6(3), 262-266.
    Silva, N., Ferreira, L. M. D. F., Silva, C., Magalhães, V., & Neto, P. (2017). Improving supply chain visibility with artificial neural networks. Procedia Manufacturing, 11, 2083-2090.
    Sun, X., Young, J., Liu, J.-H., & Newman, D. (2018). Prediction of pork loin quality using online computer vision system and artificial intelligence model. Meat Science, 140, 72-77.
    Suyantohadi, A., Mulyati, G. T., Supartono, W., & Djafar, T. F. (2001). Identification of the maturity level of mango “arumanis” using artificial neural network. IFAC Proceedings Volumes, 34(11), 325-329.
    Waller Matthew, A., & Fawcett Stanley, E. (2013). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and
    management. Journal of Business Logistics, 34(2), 77-84.
    Xie, J., Wang, Y., Zheng, X., Yang, Q., Wang, T., Zou, Y., Xing, J., & Dong, Y. (2015). Modeling and forecasting Acinetobacter Baumannii resistance to set appropriate use of cefoperazone-sulbactam: Results from trend analysis of antimicrobial consumption and development of resistance in a tertiary care hospital. American Journal of Infection Control, 43(8), 861-864.
    Yu, Z., Lei, G., Jiang, Z., & Liu, F. (2017). ARIMA modelling and forecasting of water level in the middle reach of the Yangtze River. Paper presented at the 2017 4th International Conference on Transportation Information and Safety (ICTIS).
    Zhang, M., Fulcher, J., & Scofield, R. A. (1997). Rainfall estimation using artificial neural network group. Neurocomputing, 16(2), 97-115

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