基于机器学习的临床决策支持系统的真实性是如何评估的?
Alex Poiron1, Sandie Cabon1, Marc Cuggia1
1Univ Rennes, CHU Rennes, INSERM, LTSI-UMR 1099, F-35000, Rennes, France.
Studies in health technology and informatics
|August 23, 2024
概括
在临床决策支持系统 (CDSS) 中的机器学习显示出希望,但面临评估障碍. 研究揭示了不一致的基本真相定义和不完整的度量组合,往往忽视了负面的班级表现.
科学领域:
- 临床信息学 临床信息学
- 医疗保健中的人工智能
- 医疗决策的制定 医疗决策的制定
背景情况:
- 机器学习算法为增强临床决策支持系统 (CDSS) 和改善患者护理提供了巨大的潜力.
- 然而,这些人工智能驱动系统的实际实施和可靠评估存在重大挑战.
- 评估CDSS的准确性和可靠性对于安全有效的临床采用至关重要.
结论:
- 基于机器学习的CDSS需要标准化和更严格的评估框架.
- 采用符合ISO的基本真相定义和全面的指标组合对于可靠的CDSS评估至关重要.
- 未来的评估必须明确地解决负面病例的表现,以确保可靠和可信的临床决策支持.
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