Applications of Machine Learning on Electronic Health Record Data to Combat Antibiotic Resistance

There is growing excitement about the clinical use of artificial intelligence and machine learning (ML) technologies. Advancements in computing and the accessibility of ML frameworks enable researchers to easily train predictive models using electronic health record data. However, several practical factors must be considered when employing ML on electronic health record data. We provide a primer on ML and approaches commonly taken to address these challenges. To illustrate how these approaches have been applied to address antimicrobial resistance, we review the use of electronic health record data to construct ML models for predicting pathogen carriage or infection, optimizing empiric therapy, and aiding antimicrobial stewardship tasks. ML shows promise in promoting the appropriate use of antimicrobials, although clinical deployment is limited. We conclude by describing the potential dangers of, and barriers to, implementation of ML models in the clinic. The abundance of clinical publications using machine learning models places demands on clinicians seeking to properly critique and interpret these results. This review illustrates best practices and pitfalls inherent to handling electronic health record data and employing machine learning.