Antimicrobial resistance recommendations via electronic health records with graph representation and patient population modeling.

Background: Antimicrobial resistance (AMR), which refers to the ability of pathogenic bacteria to withstand the effects of antibiotics, is a critical global health issue. Traditional methods for identifying AMRs in clinical settings rely on in-lab testing, which hampers timely medical decision-making. Moreover, there is a notable delay in updating empirical treatment guidelines in response to the rapid evolution of pathogens. Recent advances in AMR research have illuminated the potential of machine learning-based patient information analysis using electronic health records (EHRs).

Methods: Against this backdrop, our study introduces a novel deep learning framework designed to leverage EHR data for generating AMR recommendations. This framework is anchored in three critical innovations. Firstly, we employ a deep graph neural network to model the correlations between various medical events, using structural information to enhance the representation of binary medical events. Secondly, in acknowledgment of the commonalities in pathogen evolution among populations, we incorporate population-level observation by modeling patient graphical structures. This strategy also addresses the issue of imbalance in rare AMR labels. Finally, we adopt a multi-task learning strategy, enabling simultaneous recommendations on multiple AMRs. Extensive experimental evaluations on a large dataset of over 110,000 patients with urinary tract infections validate the superiority of our approach.

Results: It achieves notable improvements in areas under receiver operating characteristic curves (AUROCs) for four distinct AMR labels, with increments of 0.04, 0.02, 0.06, and 0.10 surpassing the baselines.

Conclusions: Further medical analysis underscores the efficacy of our approach, demonstrating the potential of EHR-based systems in AMR recommendation.