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使用国家报告数据验证一种自然语言处理算法,以改善与麻醉相关的ADVerse事件的识别: "ADVENTURE"研究

Paul M Mertes¹, Claire Morgand², Paul Barach³

¹Department of Anesthesia and Intensive Care, Hôpitaux Universitaires de Strasbourg, Nouvel Hôpital Civil, EA 3072, FMTS de Strasbourg, Strasbourg, France; CFAR - Collège Français des Anesthésistes-Réanimateurs, 75016 Paris, France.

Anaesthesia, critical care & pain medicine

|May 8, 2024

概括

机器学习和自然语言处理准确分析不良事件报告,提高患者安全. 这种人工智能驱动的方法与手工方法相比,可以提高风险模式的理解和决策.

科学领域:

医疗信息学医学信息学
医疗保健中的人工智能
患者安全研究患者安全研究

背景情况:

报告不良事件 (AE) 对卫生系统的学习和患者安全至关重要.
手动分析AE报告是低效的,容易出现错误.
新型机器学习 (ML) 和自然语言处理 (NLP) 方法可以自动化AE分析.

研究的目的:

证明ML和NLP的可行性,用于早期预测不良事件.
为质量改善举措提供数据驱动的见解.
通过自动化风险模式识别来提高患者安全.

主要方法:

利用ML分析9559 AE报告提交给法国国家卫生认证机构 (2009-2020年).
经过验证的AE报告标签和确定的根本原因关联.
独立的麻醉专家验证了ML模型.

主要成果:

该ML/AI模型准确地分类了88%报告的AE.
最常见的AE类型:难以输入口腔管道 (16.9%),药物错误 (10.5%),诱导后低血压 (6.9%).
人工智能模型对于特定的AE类型实现了高精度 (例如,对于难以输管的70.9%的灵敏度).

结论:

无监督的ML提供了一个准确的,自动化替代手动数据提取的AE分析.
ML和NLP模型可以有效地处理AE报告并增强临床专业知识.
该模型支持临床决策,以改善风险管理和患者安全.

关键词:

不良事件是不良事件.人工智能的人工智能是人工智能.自然语言处理自然语言处理.患者的安全性患者安全性质量改善质量改善

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