PDF(Pubmed)
Abstract：
Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.
摘要：
医疗保健相关感染（HAIs）是医疗保健中最常见的不良事件，是全球主要的公共卫生问题。监测是有效预防和控制HAIs的基础，然而，传统的监测是昂贵和劳动密集型的。人工智能（AI）和机器学习（ML）有可能支持HAI监测算法的发展，以了解HAI风险因素，改善患者风险分层以及预测和及时发现和预防感染。到目前为止，人工智能支持系统已经被探索用于临床实验室测试和成像诊断，抗菌素耐药性分析，在HAIs方面，抗生素发现和基于预测的临床决策支持工具。这篇综述旨在提供有关AI在HAIs领域应用的最新文献的全面总结，并讨论这种新兴技术在感染实践中的未来潜力。按照PRISMA准则，这项研究检查了截至2023年11月的PubMed和Scopus数据库中的文章，这些文章是根据纳入和排除标准进行筛选的。共收录162篇文章。通过阐明该领域的进展，我们的目标是强调人工智能在该领域的潜在应用，报告相关问题和不足，并讨论未来的发展方向。
