| dc.description.abstract | Improper use of antibiotics has led to the rapid development of antibiotic resistance. The current antibiotics susceptibility test (AST) which provides information of microorganism against antibiotics in clinical microbiology laboratory would spend several days. Unable to provide timely accurate prescription of antibiotics is a possible reason to the emergence of resistant bacteria. In recent years, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been widely used in clinical microbiology laboratories for rapid bacterial species identification. High accuracy bacterial species identification can be achieved without complicated and time-consuming experimental steps. Although several studies have shown that MALDI-TOF MS can be used for rapid AST, it is lack of a predictive system for identifying bacterial resistance based on mass spectrometry data. The purpose of this study is to develop a predictive system using machine learning algorithms to identify antibiotic resistant bacteria based on MALDI-TOF MS data and AST reports. First, we integrated different databases including bacterial mass spectral databases, AST report databases, and strain types databases collected from our cooperative hospitals. To integrate and obtain the value of the mass spectrum, we preprocessed the mass spectrum data. Then, we developed three methods to deal with peak shifting problems, and we explored the applicability through analysis of strain typing of Staphylococcus haemolyticus and identification of methicillin-resistant Staphylococcus aureus (MRSA). In the strain typing of application, we employed a statistical test to estimate the reference spectrum when solving peak shifting problems. We then used different machine learning algorithms to construct strain typing classifiers. The accuracy of the classifier constructed by random forest algorithm was 0.866. In the identification of MRSA application, we used binning method to deal with peak alignment issues. A large scale dataset which is different from studies in the literature was then used to construct models for identification of MRSA. Similarly, we used several machine learning algorithms to train and test the models. From the experimental results, our best model based on random forest algorithm achieved maximum area under the receiver operating characteristic curve of 0.8450 on the independent testing dataset. Finally, from these experiences, we implemented a predictive clinical system, XBugHunter, to identify antibiotic resistant bacteria for six common bacterial infections. The accuracies of clinical deployment for identifying MRSA and multi-drug resistant Acinetobacter baumannii were all higher than 90%. Compared with the current process of AST, the use of XBugHunter prediction can reduce the processing time or turn-around-time by an average of 35.72 hours. In addition, among patients with Staphylococcus aureus bacteremia, the relative risk of mortality within 28 days of the experimental group using XBugHunter was reduced by 38.4%. The predictive system for identifying bacterial resistance can provide clinicians with instant predicted ASTs, which helps clinicians prescribe appropriate antibiotics to achieve the benefits of reducing mortality, avoiding antibiotic resistance, and shortening the days in hospital. | en_US |