| Summary: | 碩士 === 國立清華大學 === 工業工程與工程管理學系 === 102 === Safety of inpatients is an important index for service quality of hospital. In-Hospital Cardiac Arrest (IHCA) is a common and high risk problem for medical institutions. The effect of Cardio-Pulmonary Resuscitation (CPR) is related to if it could save dying patients immediately and if it could increase the survival rate of patients given first aid afterward. It could discover unstable patients early and provide them medical care if we combine clinical alert system (CAS) and residents' working shifts; also, it could reduce IHCA event. Because most of the data in medical field is imbalanced data type; in addition, there is a better robustness for imbalanced data in MTS, this paper aims to analyze the data of patients in ordinary ward of case study hospital by MTS method in order to build up CAS for IHCA. After we selected the system features: diastolic blood pressure, Temperature, Pulse rate and Respiratory rate, we used the Mahalanobis-distance to be the alert standard of system. Moreover, three commonly used classification method: artificial neural network, decision tree and logistic regression, are implemented using the same data set. The result showed that there is a better classification rate in MTS for imbalanced data type.
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