In Transplant infectious disease : an official journal of the Transplantation Society
BACKGROUND : The use of machine learning (ML) in infectious diseases is expanding.
OBJECTIVES : This review aims to provide an overview of the literature on ML for clinical decision support in antimicrobial stewardship in the particular context of solid organ transplantation (SOT).
METHODS : References for this review were identified through searches of MEDLINE/PubMed and Google Scholar databases up to July 2022.
RESULTS : ML may improve the prediction of infectious complications and the diagnosis and treatment of infectious diseases in SOT recipients. One of the most studied applications for antimicrobial stewardship is the individual prediction of antimicrobial resistance that could guide the empiric use of anti-infective treatments. ML may also guide the choice of antimicrobial dose taking into account the interactions with immunosuppressive drugs. The main challenge to the development of ML clinical decision support systems (CDSSs) in SOT is the development of large clinical databases, accessible to all, with good quality, comprehensive, and diversified data. ML-driven CDSSs are still at an experimental stage, and the education of clinicians about the benefits and limits of ML is essential.
CONCLUSION : ML could improve antimicrobial stewardship for SOT, but literature on that specific topic is scarce. Future studies are needed to design ML-CDSS in the particular population of solid organ recipients and report clinical outcomes following use in routine practice.
Kherabi Yousra, Messika Jonathan, Peiffer-Smadja Nathan
antimicrobial stewardship, decision support systems, machine learning, solid organ transplantation