Navigating the future: machine learning's role in revolutionizing antimicrobial stewardship and infection prevention and control.
Curr Opin Infect Dis
; 37(4): 290-295, 2024 Aug 01.
Article
em En
| MEDLINE
| ID: mdl-38820069
ABSTRACT
PURPOSE OF REVIEW This review examines the current state and future prospects of machine learning (ML) in infection prevention and control (IPC) and antimicrobial stewardship (ASP), highlighting its potential to transform healthcare practices by enhancing the precision, efficiency, and effectiveness of interventions against infections and antimicrobial resistance. RECENT FINDINGS:
ML has shown promise in improving surveillance and detection of infections, predicting infection risk, and optimizing antimicrobial use through the development of predictive analytics, natural language processing, and personalized medicine approaches. However, challenges remain, including issues related to data quality, model interpretability, ethical considerations, and integration into clinical workflows.SUMMARY:
Despite these challenges, the future of ML in IPC and ASP is promising, with interdisciplinary collaboration identified as a key factor in overcoming existing barriers. ML's role in advancing personalized medicine, real-time disease monitoring, and effective IPC and ASP strategies signifies a pivotal shift towards safer, more efficient healthcare environments and improved patient care in the face of global antimicrobial resistance challenges.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Aprendizado de Máquina
/
Gestão de Antimicrobianos
Limite:
Humans
Idioma:
En
Revista:
Curr Opin Infect Dis
Ano de publicação:
2024
Tipo de documento:
Article