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系統識別號 U0002-2007202011033900
中文論文名稱 智慧化感染管制系統協助控制院內感染與發展醫療相關感染模型
英文論文名稱 Intelligent Infection Surveillance System to assist the Control of Healthcare-Associated Infections and Develop the Surveillance Models
校院名稱 淡江大學
系所名稱(中) 資訊工程學系博士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 108
學期 2
出版年 109
研究生中文姓名 蔡欣哲
研究生英文姓名 Hsin-Che Tsai
學號 899410038
學位類別 博士
語文別 英文
口試日期 2020-06-30
論文頁數 95頁
口試委員 指導教授-陳瑞發
委員-謝楠楨
委員-陳瑞發
委員-張志勇
委員-石貴平
委員-林偉川
中文關鍵字 醫療照護相關感染  感染管控  空間分析  健康照護資訊科技  資料探勘 
英文關鍵字 Healthcare-Associated Infections  Spatial Analysis  Healthcare Information Technology  Data Mining 
學科別分類 學科別應用科學資訊工程
中文摘要 醫療照護相關感染是健康照護的重要指標,更是導致病患罹病以及死亡的重大原因,造成醫療品質降低與增加醫療成本。本研究根據台灣衛生福利部疾病管制署規定的醫療照護相關感染的定義與標準,建立了泌尿道感染與血流感染的判定感染規則與監測系統,並提出了預測模型來預測檢體抗藥性,透過監測系統可以更早發現醫療照護相關感染的異常現象,並為感染管控人員提供檢查與輔助決策的資訊。實驗結果顯示,透過預測模型可以確定感染的重要特徵。模型預測抗藥性的準確度也相當高,並能讓感染管控人員了解現況,減少擴大感染的機會,提升抗生素的有效性。
英文摘要 Healthcare-Associated Infections (HAI) are important quality indicators of healthcare, a leading cause of mortality and morbidity worldwide, and contributors to lower medical quality and increases in medical costs. Based on the definition and determining criteria of healthcare-related infections stipulated by Taiwan’s Centers for Disease Control, Department of Health, this study created a program for an HAI determining rule, as well as an HAI monitoring system environment and proposed the HAI prediction model to predict antimicrobial resistance (AR). By using the developed system, we can discover healthcare-related infection abnormalities earlier and provide infection control professionals with the ability to check on and conduct pre-decision analyses. Prediction model experimental result shows that identified by cluster analysis of the important characteristics of HAI including sex, ward classification, department etc. Other the proposed prediction model AR with relatively satisfactory accuracy. In this study, the data mining approach for HAI control not only predicts, but also hopes to contribute a sense of control officers to immediately grasp the situation and reduce the chances of expanding infection and enhance the validity of antibiotics.
論文目次 Contents
Contents IV
List of Figures V
List of Tables VII
Chapter 1 Introduction 1
Chapter 2 Related Works 4
2.1 Healthcare - Associated Infections 4
2.2 Healthcare Information Technology 12
2.3 Data Mining 20
2.4 Development of HAI Indicators 40
Chapter 3 Intelligent Infection Surveillance System 45
3.1 Infection Monitoring 46
3.2 Indicator 49
3.3 System Interface 60
Chapter 4 Surveillance Models 64
4.1 Data Pre-processing and Conversion 65
4.2 Cluster Analysis 68
4.3 Data Mining 70
Chapter 5 Conclusion 87
References 89

List of Figures
Fig 1 Conceptual Framework 3
Fig 2 Clustering 21
Fig 3 SOM Topological Map 25
Fig 4 Bayesian network 33
Fig 5 ROC curves of the two classifiers 38
Fig 6 ROC curves of the two classifiers are very close or rugged 38
Fig 7 System development approach 40
Fig 8 Flowchart for HAI evaluation indicator development 41
Fig 9 Aggregated indexes with dashboard 44
Fig 10 Flowchart for infection control operating 45
Fig 11 System automatic monitoring process 47
Fig 12 Time analysis dimension 52
Fig 13 Department ward analysis dimension 53
Fig 14 HAI system architecture 54
Fig 15 BSI monitoring rule 55
Fig 16 UTI monitoring rule 58
Fig 17 Example code of Decision tree 59
Fig 18 Report sheet 60
Fig 19 Statistic Chart 61
Fig 20 Dashboard for infection trends 62
Fig 21 Distribution of infected patients 63
Fig 22 Model structure 65
Fig 23 Medical database 66
Fig 24 Proportion of each clusters. 69
Fig 25 Bayesian network for each clusters of drug-resistance 82
Fig 26 ROC curve for clusters-1 84
Fig 27 ROC curve for clusters-2 84
Fig 28 ROC curve for clusters-3 85
Fig 29 ROC curve for clusters-4 85

List of Tables
Table 1 Neural network algorithms [57-60] 30
Table 2 Formula for Calculating Sensitivity and Specificity 39
Table 3 The most common bacteria species in BSI and UTI in ICU 43
Table 4 Infection rate of each part in hospital 50
Table 5 Infection density of each part in hospital 51
Table 6 Selected prediction variables 67
Table 7 Selected variables used in the cluster analysis 68
Table 8 Interviewee factors of each clusters 69
Table 9 Variable and target value of resistant bacteria 70
Table 10 The significant values of the variable relationships 72
Table 11 The variables in clusters-1 and clusters-2 of resistant bacteria 75
Table 12 The variables in clusters-3 and clusters-4 of resistant bacteria 76
Table 13 ANOVA for each clusters of drug-resistance 77
Table 14 Classification results 80
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