在基于计划的自动化方法中提供和评估解释,以解决多病症问题
Martin Michalowski1, Szymon Wilk2, Wojtek Michalowski3
1University of Minnesota, 308 Harvard St SE, Minneapolis, MN 55455, USA.
Journal of biomedical informatics
|July 3, 2024
概括
这项研究引入了用于多病症自动化治疗计划的可解释性组件,为医生提供了治疗决策的明确临床理由. 开发的解释被发现是可解释的,并且对医疗专业人士有价值.
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 临床决策支持
背景情况:
- 多病症问题需要从多个临床指南中管理复杂的相互作用.
- 医生很难理解多病症的自动决策支持.
- 治疗计划的明确理由对于医生信任和采用至关重要.
研究的目的:
- 开发一个可解释性组件,用于多病症的自动化规划.
- 评估生成的解释的真实性和可解释性.
- 在临床案例研究中评估组件的实用性.
主要方法:
- 利用任务网络模型为计算机可解释的指导方针.
- 开发了一个MitPlan组件,为行动的逻辑和影响因素提供解释.
- 对21名参与者进行了以医生为中心的评估研究.
主要成果:
- 可解释性组件在 MitPlan.Plan 中成功实现.
- 解释显示了可接受的真实性和可解释性.
- 医生发现治疗行动的临床理由很重要.
结论:
- 创建了一个可解释性组件,以增强自动化多病症规划,并提供有意义的理由.
- 该组件使用任务网络模型,可适应类似系统.
- 支持医生理解治疗行动的解释是有价值的.
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