# Applying Machine Learning Techniques to the Audit of Antimicrobial Prophylaxis
## 4 Discussion
Although these instances could be identified as inappropriate use, the models that used machine learning techniques to audit antimicrobial prophylaxis do not override clinical judgment. Once the surgeons obtain the information from the audit, they can review the cases of nonadherence to guideline. Lack of documentation of the reason of antimicrobial prescription is not uncommon (about 20% in European hospitals) [32]. If the variances from standards arise from lack of documentation of pre-existing infection and treatment for infection rather surgical prophylaxis, they can improve the documentation of treatment or surgical prophylaxis on the medical records. In the future study, the instances with treatment for preexisting infection will be excluded to reduce the variances from standards. Second, the small population of outliers used in training set may not sufficiently represent the outlier patterns. The small amount of outlier samples can limit the capability of building an accurate classifier for the detection of outlier patterns, and it may also result in misclassification. Third, before administering any antibiotic prophylaxis regimen, it is necessary to define whether the patient really needs such prophylaxis. Antimicrobial prophylaxis is not recommended for most clean surgical procedures in patients without risk of postoperative infection, such as thyroidectomy or clean plastic surgery [7]. However, the risks of infection for these clean procedures were not included in the attributes. Consensus on this issue is required from the surgical society as well as adding infection risks of surgery to the attributes. Only then will the algorithms be able to provide more useful information for assessing the appropriate antimicrobial use in future studies. Fourth, some features considered in this study were not relevant. Irrelevant features can have an impact on the model performance. Regularization strategy is proposed to enhance the model performance by removing the useless features and selecting only the useful features from the model, and minimizing the weights of the features with little useful information on the classification [35]. In future work, we will focus on the database design and configuration of more useful data to improve the performance of the algorithms. Fifth, we are concerned about the issue of inappropriate surgical prophylaxis or limited antimicrobial options leading to the selection of resistant bacteria. Some strategies were proposed to prevent the emergence of resistant bacteria, such as combination regimens [38; 39] and adequate dosing to maintain the serum concentration of antibiotic above the minimal inhibitory concentration of the bacteria during the treatment [39; 40]. Machine learning may be used to deal with this issue, which involves alternative surgical prophylactic regimens and emergence of resistant bacteria, based on the hypothesis that the new resistant bacteria may be prevented or treated with new prophylactic regimens. Reinforcement learning is an approach that seems suitable for such a problem. However, more data on antimicrobial resistant patterns and alternative options for surgical prophylactic antimicrobial agents need to be collected before validating reinforcement learning for this issue in future studies.