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系統識別號 U0002-2009201723342000
中文論文名稱 數據分析管理系統在心臟外科的應用
英文論文名稱 Data Analysis Management System in Cardiac Surgery
校院名稱 淡江大學
系所名稱(中) 資訊工程學系全英語碩士班
系所名稱(英) Master’s Program, Department of Computer Science and Information Engineering (English-taught program
學年度 105
學期 2
出版年 106
研究生中文姓名 謝宗翰
研究生英文姓名 Zong-Han Xie
學號 604780063
學位類別 碩士
語文別 中文
第二語文別 英文
口試日期 2017-07-19
論文頁數 76頁
口試委員 指導教授-葛煥昭
委員-葛煥昭
委員-施俊哲
委員-陳瑞發
中文關鍵字 周邊動脈阻塞性疾病  主動脈瘤  資料探勘 
英文關鍵字 Peripheral Arterial Occlusive Disease  Aortic aneurysms  Data Mining 
學科別分類 學科別應用科學資訊工程
中文摘要 臺灣醫院資料庫的儲存量是相當龐大的,資料也非常多元並且複雜,而這些資料並非所有資料都被需要,把大資料使用個別獨立分析的小型資料集,然後再將各個資料群集合併進行分析,找出資料的關聯性以及從而尋找出隱藏在大型資料庫中的有價值的資訊。
在本次研究之中,整合主動脈瘤與周邊動脈阻塞性疾病兩種疾病,依照疾病的不同建立相對應的資料表,醫護人員能夠利用數據分析管理系統更簡單的紀錄病患的病歷資料,減少醫護人員的工作量使醫護人員能夠運用更多的時間在照護病人上面,並且醫護人員能夠利用本研究所建立的數據分析管理系統,進行相對應疾病的個別分析與雙疾病交叉分析,根據疾病與疾病所記錄相同的資料項目進行交叉分析並產生分析圖表,醫護人員則能夠觀察圖表找出資料與資料之間的關聯性,進而獲取隱藏在資料背後的資訊。
英文摘要 Taiwan hospital database is very large. Data which stored in the hospital database is also very diverse and complex, and these data are not all data are needed. The individual data set will be analyzed in the big data, and the data set will be integrated and then analyze again to find the relationship and the hidden knowledge in the database.
In the research, we integrated the aortic aneurysm with peripheral arterial occlusive disease in the database, and established the corresponding data table with the different diseases. Medical staff can use the data analysis and management system to record medical data of patients more easily, and also reduce the workload of medical staff. Medical staff can spend more time in caring for patients. Medical staff can make the use of the data analysis and management system established by the Institute to do the individual analysis of the corresponding diseases and the cross analysis of the two diseases. According to the different diseases recorded in the same data item cross analysis and chart analysis, the medical staff can observe the charts to identify correlation between data and data, and then obtain the information hidden in the data behind. Medical staff observe and analyze charts to obtain information hidden behind the data.
論文目次 Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Purpose 3
Chapter 2 Literature Review 4
2.1 Cloud Clinical Diagnostics Aid System of Division of Cardiovascular Surgery 4
2.2 Data Mining 6
2.2.1 Introduction to data mining 6
2.2.2 Knowledge Discovery in Database 6
2.2.3 Data Mining Techniques 9
2.2.4 Data Mining Approach 10
2.2.5 Application of data mining in the medical 20
2.3 Aortic aneurysm 20
2.3.1 Introduction of the Aortic aneurysm 20
2.3.2 Classification of aortic aneurysms 22
2.4 Peripheral Arterial Occlusive Disease 24
2.4.1 Fontaine Classification 25
2.4.2 Clinical diagnosis and treatment 26
Chapter 3 Method 28
3.1 Research Process 28
3.2 System Architecture 30
3.2.1 Development Environment 30
3.2.2 Design of the System Architecture 32
3.2.3 Design of system model and table of database 33
3.3 System Analysis 36
Chapter 4 Results and case analysis 40
4.1 System establishment 40
4.1.1 Data input and modification module 40
4.1.2 Database search module 44
4.1.3 Analysis module 50
4.1.4 Export module 56
4.2 Case analysis 56
4.2.1 Overall data analysis 57
4.2.2 Preoperative and postoperative track analysis 61
4.2.3 PAOD/AA data analysis 68
Chapter 5 Conclusions 71
5.1 Conclusions 71
5.2 Future work 72
Reference 74

List of Figures
Figure 1. KDD Process 8
Figure 2 Apriori flow chart 15
Figure 3 the flow charts of genetic algorithm 18
Figure 4 K-means flow chart 19
Figure 5 Guidelines for the diagnosis of ESC aortic disease, 2014 22
Figure 6 Process of the system development 30
Figure 7 three tiered network architecture 33
Figure 8 DB table of the AAA 35
Figure 9 DB table of the DAA 35
Figure 10 DB Table of the TAA 36
Figure 11 DB Table of the PAOD 36
Figure 12 Process of user’s operating 39
Figure 13 Select disease type and method of the medical record input (AAA as an example) 42
Figure 14 medical record input 42
Figure 15 Enter the date of operation and establish a medical record 42
Figure 16 Enter medical records (take AAA as an example) 43
Figure 17 Medical record number search engine 45
Figure 18 the medical record result of the search sample ID 46
Figure 19 select the disease (make DAA as a sample) 46
Figure 20 the list of result search of the 2 times or more 47
Figure 21 select condition items (make PAOD as a sample) 48
Figure 22 Enter the range value of the item(PAOD) 48
Figure 23 the result of the search 48
Figure 24 select date range and the type of disease (make DAA as the sample) 49
Figure 25 the result of the search 50
Figure 26 select disease and data type (make AAA as the sample) 51
Figure 27 select analysis condition (make AAA as the sample) 52
Figure 28 enter the data condition of the search 52
Figure 29 select data attribute 52
Figure 30 the relationship between WBC and age 52
Figure 31 select disease and the analysis object 54
Figure 32 select patient data (individual patient analysis) 54
Figure 33 select data attribute 54
Figure 34 result chart 55
Figure 35 PAOD and AA data analysis 56
Figure 36 export interface 56
Figure 37 Age and WBC line chart 59
Figure 38 Age and CHOL line chart 60
Figure 39 WBC average values before surgery and after surgery (straight bar chart) 64
Figure 40 Platelet values before and after surgery (straight bar chart) 65
Figure 41 WBC data before and after surgery (line chart) 66
Figure 42 Platelet value before and after surgery (line chart) 67
Figure 43 PAOD and AAA's Age/WBC line chart 69
Figure 44 PAOD/AAA’s age and triglyceride line chart 70

List of Table
Table 1. KDD Process 7
Table 2 Data mining Techniques 9
Table 3 Fontaine classification 25
Table 4 Data Item of the medical record 43

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