| 研究生: |
江欣怡 Chiang, Hsini-i |
|---|---|
| 論文名稱: |
以類神經網路預測成人加護病房抗藥性金黃色葡萄球菌移生的病人 Prediction of MRSA Using Artificial Neural Network among Patients Admission to Adult ICUs |
| 指導教授: |
柯乃熒
Ko, Nai-Ying |
| 學位類別: |
碩士 Master |
| 系所名稱: |
醫學院 - 護理學系 Department of Nursing |
| 論文出版年: | 2008 |
| 畢業學年度: | 96 |
| 語文別: | 中文 |
| 論文頁數: | 58 |
| 中文關鍵詞: | 加護病房院內感染 、類神經網路 、MRSA帶菌者 |
| 外文關鍵詞: | MRSA carriages, ICU nosocomial infection, Artificial Neural Network |
| 相關次數: | 點閱:81 下載:2 |
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從1960年代初期至今,抗藥性金黃色葡萄球菌(methicillin resistant Staphylococcus aureus, MRSA)為北美、歐洲及台灣院內感染的主要致病原,而加護病房被證實為醫院間及醫院內MRSA傳播的主要場域。研究目的為發展類神經網路預測成人加護病房MRSA帶菌病人,依據實驗室診斷結果,比較類神經網路預測MRSA帶菌的敏感性、特異性以及ROC curve,以驗證類神經網路預測MRSA帶菌病人之正確率。採前瞻性研究設計,針對南部某1000床之醫學中心住進成人加護病房區共201位病人進行MRSA帶菌篩檢,且經由鼻腔檢體確立為MRSA帶菌病人,同時收集病人的人口學資料、臨床及實驗室檢驗資料,以作為進行類神經網路預測MRSA帶菌者塑模的訓練資料;將樣本分成100、120、140、160、180筆的訓練資料進行類神經網路的塑模,並且分別使用未放入訓練的資料以進行模式預測力的驗證。結果發現成人加護病房的MRSA帶菌者的盛行率為11.9%。類神經網路預測MRSA帶菌者的敏感性、特異性以及正確率會隨著樣本數的增加而提高;當訓練樣本數從100筆增加到180筆時,其敏感性平均可從32.79%提高至80%以上,且特異性以及正確率可維持在95.5%及97.22%。因此透過使用類神經網路模式塑模來權重MRSA帶菌者的危險因子,以預測成人加護病房的MRSA帶菌者,為有效、即時、快速以及可成為替代主動篩檢的選擇性診斷策略之ㄧ。
Methacillin-resistant Staphylococcus aureus (MRSA) has been recognized as the most important pathogen of nosocomial infections in North America, Europe, and Taiwan. Intensive care units (ICU) play a major role in the spread of MRSA infection in hospitals. This study was developed a trained artificial neural network (ANN) model that accurately predicted the likelihood of MRSA carriages at the time of admission to adult ICUs. Patients who admitted to ICU within 24 hours were screened for nasal MRSA colonization and risk factors associated with MRSA carriages were collected. The laboratory results and collected risk factors were used to train the artificial neural network (ANN) model. Based on the laboratory diagnosis, sensitivity, specificity, accuracy, and ROC curve were used to validate the performances of the ANN models. A total of 201 patients were enrolled. The 201 samples were divided into two data sets for training and validation uses. The 201 samples were randomly selected as 50%, 60%, 70%, 80%, 90% to train the ANN model respectively, and the remaining data were used to validate the performance of the well-trained ANN model by taking the laboratory results as the golden standard for the diagnosis of MRSA colonization.
The prevalence rate of MRSA carriages in adult ICUs is 11.9%. The accuracy of the trained ANN models were enhanced along with the samples increased. When the trained samples were increasing from 100 to 180 samples, the sensitivity was improved from 32.79% to 80% and the specificity and the accuracy reached to 95.5% and 97.22%. The well-trained validated ANN model can predict MRSA carriages accurately and may be an effective, immediate, timely alternative to carry out the active surveillance. Applying the trained ANN model to quickly diagnose and implement efficient infection control programs to reduce the medical costs and nosocomial infections in adult ICUs.
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