{"title":"Navigating the future: machine learning's role in revolutionizing antimicrobial stewardship and infection prevention and control.","authors":"John J Hanna, Richard J Medford","doi":"10.1097/QCO.0000000000001028","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose of review: </strong>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.</p><p><strong>Recent findings: </strong>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.</p><p><strong>Summary: </strong>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.</p>","PeriodicalId":10880,"journal":{"name":"Current Opinion in Infectious Diseases","volume":" ","pages":"290-295"},"PeriodicalIF":3.6000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11211045/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Opinion in Infectious Diseases","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/QCO.0000000000001028","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/31 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"INFECTIOUS DISEASES","Score":null,"Total":0}
引用次数: 0
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.
综述的目的:本综述探讨了机器学习(ML)在感染预防与控制(IPC)和抗菌药物管理(ASP)方面的现状和未来前景,强调了其通过提高抗感染和抗菌药物耐药性干预措施的精确性、效率和有效性来改变医疗保健实践的潜力:最近的研究结果:通过开发预测分析、自然语言处理和个性化医疗方法,ML 在改善感染监控和检测、预测感染风险和优化抗菌药物使用方面大有可为。然而,挑战依然存在,包括与数据质量、模型可解释性、伦理考虑以及与临床工作流程的整合有关的问题。总结:尽管存在这些挑战,但 ML 在 IPC 和 ASP 中的应用前景广阔,跨学科合作被认为是克服现有障碍的关键因素。面对全球抗菌药耐药性的挑战,ML 在推动个性化医疗、实时疾病监测以及有效的 IPC 和 ASP 战略方面的作用标志着向更安全、更高效的医疗环境和更好的患者护理转变的关键。
期刊介绍:
This reader-friendly, bimonthly resource provides a powerful, broad-based perspective on the most important advances from throughout the world literature. Featuring renowned guest editors and focusing exclusively on two topics, every issue of Current Opinion in Infectious Disease delivers unvarnished, expert assessments of developments from the previous year. Insightful editorials and on-the-mark invited reviews cover key subjects such as HIV infection and AIDS; skin and soft tissue infections; respiratory infections; paediatric and neonatal infections; gastrointestinal infections; tropical and travel-associated diseases; and antimicrobial agents.