||Intelligent Infection Surveillance System to assist the Control of Healthcare-Associated Infections and Develop the Surveillance Models
||Department of Computer Science and Information Engineering
Healthcare Information Technology
||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.
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
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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