Applying Machine Learning Techniques to the Audit of Antimicrobial Prophylaxis

Abstract

High rates of inappropriate use of surgical antimicrobial prophylaxis were reported in many countries. Auditing the prophylactic antimicrobial use in enormous medical records by manual review is labor-intensive and time-consuming. The purpose of this study is to develop accurate and efficient machine learning models for auditing appropriate surgical antimicrobial prophylaxis. The supervised machine learning classifiers (Auto-WEKA, multilayer perceptron, decision tree, SimpleLogistic, Bagging, and AdaBoost) were applied to an antimicrobial prophylaxis dataset, which contained 601 instances with 26 attributes. Multilayer perceptron, SimpleLogistic selected by Auto-WEKA, and decision tree algorithms had outstanding discrimination with weighted average AUC > 0.97. The Bagging and SMOTE algorithms could improve the predictive performance of decision tree against imbalanced datasets. Although with better performance measures, multilayer perceptron and Auto-WEKA took more execution time as compared with that of other algorithms. Multilayer perceptron, SimpleLogistic, and decision tree algorithms have outstanding performance measures for identifying the appropriateness of surgical prophylaxis. The efficient models developed by machine learning can be used to assist the antimicrobial stewardship team in the audit of surgical antimicrobial prophylaxis. In future research, we still have the challenges and opportunities of enriching our datasets with more useful clinical information to improve the performance of the algorithms.

Sustaining Improvements of Surgical Site Infections by Six Sigma DMAIC Approach

“…However, auditing the performance measures by manual review of the medical records is labor-intensive and time-consuming. The machine learning techniques can provide accurate and efficient models for audit of the performance measures; for example, The machine learning techniques could be applied for the audit of appropriate prophylactic antimicrobial use [ 40 ]. The algorithms of machine learning techniques are more efficient in execution time than manual review.…”

Brave New World of Artificial Intelligence: Its Use in Antimicrobial Stewardship—A Systematic Review

“…In these studies, no information was provided regarding how missing data had been handled. Overall, only one study was rated as having a low ROB [24]. Regarding applicability, one study ranked as being of "high concern" and as having a high ROB due to the lack of participant information and lack of definition of the inclusion and exclusion criteria [27].…”

“…A total of 4658 citations were identified from the three databases and, after removing the duplicates, 2839 were eligible for screening. A total of 1086 articles were assessed for eligibility and eighteen [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37] were included in this systematic review (Figure 1). Most studies were excluded because they did not study the application of machine learning models nor their predictive performance or because they were not applied to hospital inpatients and outpatients with infections, such as studies in vitro or regarding drug development.…”

“…The risk of bias (ROB) and the applicability for model prediction of the eighteen included studies were also assessed by PROBAST (Table 4). Only two studies were ranked as being of "low concern" in the analysis domain [22,24]; six studies were defined as being of "unclear concern" [20,21,23,25,26,29], and ten were ranked as being of "high concern" [27,28,[30][31][32][33][34][35][36][37]. In these studies, no information was provided regarding how missing data had been handled.…”

“…The other main results are available in Table 1. All the studies concluded that ML algorithms were useful to assist antimicrobial stewardship teams in multiple tasks such as identifying inappropriate prescribing practices [20], choosing the appropriate antibiotic therapy [22,23,34,36], auditing surgical antimicrobial prophylaxis [24], predicting personal risk of treatment-induced emergence of resistance [25], estimating patient outcomes under the contrasting scenarios of stopping or continuing antibiotic treatment [26], predicting AMR [27], and identifying patients at low risk of bacterial infections [29].…”