人工智能用于改善细菌感染管理中的决策:叙述性审查
Anisia Talianu1,2, Oskar Fraser-Krauss1,2, William Bolton1,2
1Faculty of Engineering, Department of Computing, Imperial College London, London, UK.
人工智能 (AI) 和机器学习 (ML) 增强了针对细菌感染的临床决策支持系统 (CDSS). 然而,AI-CDSS面临着整合的挑战,需要适应性,整体的方法来改善抗生素管理.
科学领域:
- 传染病管理 传染病管理
- 临床决策支持系统 临床决策支持系统
- 医疗保健中的人工智能
背景情况:
- 临床决策支持系统 (CDSS) 已经发展了60多年,最近的进展包括人工智能 (AI) 和机器学习 (ML).
- 智能CDSS在解释复杂数据和指导感染管理临床决策方面表现有前途.
研究的目的:
- 审查当前的AI驱动的CDSS应用在细菌感染管理中,从预防到治疗个性化.
- 确定阻碍临床翻译的局限性,并建议AI-CDSS开发的改进.
主要方法:
- 在2025年3月之前使用PubMed,谷歌学者,bioRxiv和arXiv进行文学评论.
- 关键词包括机器学习,决策和细菌感染.
主要成果:
- 人工智能CDSS研究越来越多地使用多式联络电子健康记录 (EHR) 数据,更简单的模型在结构化数据上表现良好.
- 尽管AI-CDSS在一些任务中达到临床医生水平的准确性,但由于范围狭窄,工作流被忽视和评估不足,AI-CDSS的临床整合有限.
- 挑战包括医疗任务的范围有限,无法整合到临床工作流程中,以及缺乏适当的评估框架.
结论:
- 需要转向适应性,整体的CDSS开发,以应对感染管理决策的持续性质.
- 模拟感染动态的全面AI平台可以增强抗生素管理和打击抗菌素耐药性.
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