# Applying Machine Learning Techniques to the Audit of Antimicrobial Prophylaxis
## 1 Introduction  
The incidence of surgical site infections (SSIs) is estimated to be ~2-5% in patients undergoing surgery [1]. SSIs are SSI are associated with increased rates of morbidity and mortality. The financial impact of SSIs is the highest among all healthcare-associated infections. The annual cost of SSIs in the United States of America is estimated to be $3.5-10 billion [2]. About 40-60% of SSIs are preventable by the interventions with evidence-based measures [1, 2]. The interventions include antimicrobial prophylaxis, preoperative bathing and showering, glucose control, skin preparation, intraoperative normothermia, and wound closure [1, 3, 4, 5].  
Appropriate antimicrobial prophylaxis is the most important and strongly recommended measure to reduce the SSI rates [2, 3, 6]. To achieve these goals, appropriate practices of preoperative-administration timing, choice and dosing of antimicrobial agents, and duration of antimicrobial prophylaxis are the most important approaches [7]. However,previous studies showed that nonadherence to the guidelines of antimicrobial prophylaxis is a worldwide problem and ranges from 20% to 50% [5; 8; 9].  
Antimicrobial stewardship is an emerging global action plan on antimicrobial resistance. Antimicrobial stewardship implements a series of strategies and interventions to improve appropriate use of antimicrobial agents in healthcare settings [10; 11]. The interventions include formulary restriction, audit and feedback, education for prescribers, clinical guidelines, and clinical decision support systems provided by information technology. Audit is the most common intervention of all antimicrobial stewardship strategies. However, the processing and analysis of enormous medical data for the manual audit is time-consuming and labor-intensive. Therefore, it is helpful to develop efficient models by machine learning to analyze the big medical data associated with antimicrobial prophylaxis.  
Machine learning creates new and promising opportunities in processing enormous volume of data to provide data classification, clustering, outlier or anomaly detection, and real-time prediction [12]. Machine learning can help clinicians to diagnose infectious diseases, evaluate severity of the diseases, and decide the choice of antimicrobial agents [13; 14]. In a narrative review of 60 articles of machine learning for clinical decision support systems, 37 (62%) articles focused on bacterial infections, 10 (17%) on viral infections, and 13 (22%) on other kinds of infections. Among the 60 articles, four (7%) articles addressed the prediction of antimicrobial resistance, and three (5%) addressed the selection of antimicrobial agents [13]. The popular supervised machine learning algorithms, multilayer perceptron and decision tree, were applied to the antimicrobial susceptibility dataset to predict the resistant profile of bacteria, thereby aiding the clinicians in selecting empiric antimicrobial therapy [15; 16]. However, they had the limitations of features selection and imbalanced dataset. Application of machine learning to the audit of antimicrobial prophylaxis was never reported. There is strong potential for research to develop machine learning algorithms with excellent performance in this field.  
The purpose of this study is to develop accurate and efficient machine learning models for auditing appropriate surgical antimicrobial prophylaxis. Auto-WEKA, multilayer perceptron, and decision tree algorithms were applied to the dataset of surveillance of healthcare-associated infections and antimicrobial use. Sampling and ensemble methods were used to enhance the performance against the imbalanced dataset. We compared the performance measures of these algorithms for the audit of antimicrobial prophylaxis.