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歷年稿件內容

*類別：	F組-其他
* 姓名：	林怡萱
投稿種類：	壁報
*中文投稿標題：	以資料採礦技術進行抗藥性金黃色葡萄球菌感染者之臨床照護與管理
*中文作者姓名列：	林怡萱、林裕森、林尊湄
*中文服務單位：	義大醫療財團法人義大醫院 , 國立高雄師範大學
*英文投稿標題：	Use of data mining techniques in clinical care and management of patients with Methicillin-Resistant Staphylococcus aureus (MRSA) infections
*英文作者姓名列：	Lin, I-hsuan；Lin, Yu-sen；Lin Tsun-Mei
*英文服務單位：	E-DA Hospital,National Kaohsiung Normal University
* 投稿摘要：	Methicillin-resistant Staphylococcus aureus (MRSA) is the most attention of a multi-drug-resistant pathogenic bacteria. Different strains of S. aureus can produce toxins cause skin, wounds, osteomyelitis, pneumonia, bacteremia, and other infections. Infection is mainly spread through direct physical contact, skin wounds, crowded environment, and poor personal hygiene may also cause infection. MRSA can colonize different parts of the body, including the nose and on the skin, report noted that patients infected with MRSA bacteremia infection within 30 days mortality was 34%. Microbial culture and antibiotic sensitivity test are tests to confirm the diagnosis of MRSA. From mining inspection, microbiological culture, bacteria were identified and drug sensitivity test report completed at least 72 hours. The study is based on data mining techniques to predict patient MRSA bacteremia, the patient remains positive bacteremia, and the chances of receiving a traditional vancomycin therapy after 7 days, 30 days, and death. The purpose is to be more rapid than traditional culture methods offer clinicians as basis for antibiotic treatment. In this study, we used 29 risk factors. Our results showed that we are able to predict the 7-day persistence of MRSA in blood cultures at accuracy ranged 82.0% ~ 86.6%; death of patient within 30 days at accuracy ranged 53.4% ~ 69.2%. Such a prediction method can be applied in any hospital, the use of a prospective study to collect patient data in order to establish their predictive models.
*關鍵字1 ：	Methicillin-Resistant Staphylococcus aureus
*關鍵字2 ：	Data mining techniques
*關鍵字3 ：	Risk factors
*關鍵字4 ：	none
*關鍵字5 ：	none
* 服務機關：
* 第一作者：	林怡萱
* 身分字號：	*****48285
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審查委員意見：	OK
審查委員意見：	ok
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