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

Intelligent Infection Surveillance System to assist the Control of Healthcare-Associated Infections and Develop the Surveillance Models

蔡欣哲(Hsin-Che Tsai)

指導教授 ： 陳瑞發

淡江大學/工學院/資訊工程學系博士班/博士(2020年)

https://doi.org/10.6846/TKU.2020.00570

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。

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

並列摘要

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.

並列關鍵字

Healthcare-Associated Infections ； Spatial Analysis ； Healthcare Information Technology ； Data Mining

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