在外围插入的中央导管 (PICC) 中对血栓和感染风险因素的基于人工智能的分析
Bénédicte Rouvière1, Florian Berteau2, Nathan Foulquier3
1Département de médecine interne, pneumologie et médecine vasculaire, CHU de Brest, Hôpital La Cavale-Blanche, Brest cedex, France; U1227, LBAI, Université de Brest, Inserm, CHU de Brest, Brest, France.
概括
像BIBOT这样的人工智能工具可以加速识别外围插入中央导管 (PICC) 并发症的危险因素,例如感染和血栓形成. 人类验证对于医学文献审查的准确性至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 血管接入装置 血管接入装置
- 临床风险管理临床风险管理
背景情况:
- 周围插入的中央导管 (PICC) 广泛用于长时间的静脉注射疗法.
- 虽然比传统的中央静脉导管更容易插入,但PICC与感染和血栓形成等延迟并发症有关.
研究的目的:
- 确定外围插入中央导管 (PICC) 相关感染和血栓形成的危险因素.
- 评估人工智能 (AI) 工具在医学文献审查中对识别这些风险因素的有用性.
主要方法:
- 使用PubMed (2013-2023) 进行了一项文献综述.
- 一个人工智能程序 (BIBOT) 和LLaMA3被用于自然语言处理和抽象选.
- 人工智能辅助的内容分析确定了相关的文章,随后是手动识别风险因素.
主要成果:
- 人工智能选处理了1896篇摘要,确定了343篇相关文章.
- 确定了20种传染性和59种血栓形成的危险因素.
- 关键的感染风险:流明数,化疗,停留时间. 关键的血栓形成风险:癌症,化疗,PICC直径>2流明.
结论:
- 基于人工智能的工具显著加快了PICC并发症的文献审查和数据提取.
- 人类验证对于确保准确性至关重要,特别是在复杂的医学术语方面.
- 混合人工智能-人类方法为医学研究提供了高效可靠的方法.
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