金黃色葡萄球菌（Staphylococcus aureus）是一種革蘭氏陽性球型細菌，為常見感染菌種之一，其中，有「超級細菌」之稱的耐甲氧西林金黃色葡萄球菌對大部分的青黴素類抗生素皆會產生抗藥性，如未能及早施用正確抗生素治療，嚴重可能導致死亡。傳統微生物檢驗方法檢測抗藥性需要數天，無法及時給予最適當之抗生素，因此，及時提供抗藥性之訊息，並施用適當的藥物治療，可降低死亡率以及避免抗藥性之發生。臨床微生物檢驗近年來已廣泛採用基質輔助雷射脫附電離飛行時間質譜法（Matrix-Assisted Laser Desorption Ionization-Time-of- Flight Mass Spectrometry，MALDI-TOF MS）進行微生物之鑑定，許多研究亦依此資料辨識細菌之抗藥性，然而，目前仍缺乏以大量臨床數據建構辨識金黃色葡萄球菌抗藥模型，本研究藉由長庚醫院多年蒐集之臨床金黃色葡萄球菌質譜資料，結合機器學習快速分類此菌株對苯唑青黴素、克林達黴素與紅黴素的抗藥情形。本研究採用決策樹、隨機森林與支持向量機建構辨識模型，其中，隨機森林分類器輔以向前特徵選取法所建構之模型準確率最高。辨識苯唑青黴素、克林達黴素與紅黴素抗藥性在獨立測試集的準確率分別為80.56%、82.42%與74.71%。本研究根據質譜資料所建立之辨識金黃色葡萄球菌抗藥模型可及時提供臨床醫師施用抗生素之相關資訊，進而提供病人更適切的照護。;Staphylococcus aureus is a Gram-positive globular bacterium that is a flora common to the epidermis, but often causes opportunistic infections and is one of the common infectious strains. Among them, methicillin-resistant Staphylococcus aureus, known as "superbug", is a common infectious strain and is resistant to most penicillin antibiotics. Traditional identification of drug resistance often takes about three days, which is usually dependent on the physician′s empirical therapy. If the drug resistance is identified early and treated with appropriate drugs, it will greatly improve the effect of treatment. The purpose of this study was to use the mass spectrum of the strain obtained by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, and combine the machine learning to quickly classify the resistance of this strain to oxacillin, clindamycin and erythromycin. This study uses decision trees, random forests and support vector machine classifiers to make prediction models, and finds that using random forest classifiers with sequential forward selection to select features can have the highest accuracy. The accuracy of predicting oxacillin, clindamycin and erythromycin resistance on independent test set were 80.56%, 82.42% and 74.71%, respectively. The results predicted by our model can provide a timely reference for clinicians, and provide more appropriate care for patients.
