臺灣博碩士論文加值系統

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

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