一个数据驱动的临床决策支持框架,应用于肺炎管理
Robert C Free1, Daniel Lozano Rojas2, Matthew Richardson2
1School of Computing and Mathematical Sciences, University of Leicester, Leicester, United Kingdom.
Frontiers in digital health
|October 25, 2023
概括
一个新的框架简化了将人工智能 (AI) 和机器学习 (ML) 临床决策支持工具嵌入卫生系统. 这通过可视化人工智能预测来管理社区获得的肺炎,提高患者优先级来增强信任.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
背景情况:
- 将临床决策支持 (CDS) 工具,特别是使用机器学习 (ML) 和人工智能 (AI) 的工具整合到健康信息系统中仍然具有挑战性.
- 复杂的AI/ML模型缺乏透明度,通常被认为是"黑子",可能会阻碍用户的信任和医疗保健专业人员的采用.
- 社区获得性肺炎 (CAP) 是住院死亡的一个重要原因,需要有效的管理策略和决策支持.
研究的目的:
- 介绍和评估一个以数据为导向的框架,旨在克服嵌入基于AI/ML的CDS工具的挑战.
- 展示该框架在开发和测试用于医院内CAP管理的CDS工具中的应用.
- 为了说明这些工具如何被整合到临床系统中,以增强可视化,以提高理解和信任.
主要方法:
- 开发了一个数据驱动的框架,以解决基于AI/ML的CDS的可信性和信任问题.
- 应用框架创建一个CDS工具来管理社区获得的肺炎,与当地指导方针保持一致.
- 将CDS工具集成到原型临床系统中,将患者旅程和风险评估的指标和可视化纳入其中.
主要成果:
- 开发的框架成功地应对了将复杂的AI/ML模型嵌入到临床工作流程中的挑战.
- 该CDS工具在以后优先考虑患者进行CAP管理方面表现出有效性.
- 整合到原型系统中为决策者提供了对患者数据,风险评分和AI预测的视觉见解.
结论:
- 数据驱动的框架显示了开发,测试和评估工作流集成的CDS工具的巨大潜力,这些工具包含复杂的AI/ML模型.
- 从框架中获得的可视化和指标增强了医疗保健提供者对AI/ML预测的解释性和可用性.
- 这种方法有助于将先进的AI/ML能力无嵌入到临床系统中,培养信任并改善疾病管理.
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