可解释的人工智能用于临床结果预测:对临床医生的看法和偏好进行调查
1Virginia Tech, Blacksburg, VA.
概括
临床医生更喜欢不同的可解释AI (XAI) 技术来解释来自电子健康记录 (EHR) 的AI预测. 结果指导选择适当的XAI方法用于临床决策.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 可解释性AI (XAI) 对于将AI预测集成到临床工作流中至关重要.
- 临床医生的信任和对AI输出的理解取决于可解释的方法.
- 基于文本的电子健康记录 (EHR) 数据对AI解释提出了独特的挑战.
研究的目的:
- 评估临床医生对EHR数据应用的各种XAI技术的偏好.
- 了解不同的XAI方法如何影响AI驱动的死亡率预测的解释.
- 为临床实践选择和改进XAI工具提供指导.
主要方法:
- 实施了四种XAI技术:LIME,基于注意力的跨度亮点,示例患者检索和LLM生成的理性.
- 开发了一个使用ICU入院笔记预测医院死亡率的结果预测模型.
- 对32名执业临床医生进行了调查研究,以收集有关XAI技术偏好的反.
主要成果:
- 对四种评估的XAI技术确定了不同的临床医生偏好.
- 收集了关于每个XAI方法的可用性和可解释性的定性反.
- 突出了某些XAI技术比其他技术更受青的特定场景.
结论:
- 临床医生对XAI技术的偏好因特定的应用和数据类型而异.
- 这些发现有助于开发更有效和以用户为中心的XAI工具,用于医疗保健.
- 提供了优化XAI技术选择和在临床环境中实施的建议.
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