ROAD2H:开发和评估一种开源可解释的人工智能方法,用于管理并发病症和临床指南
Jesús Domínguez1, Denys Prociuk2, Branko Marović3
1Department of Population Health Sciences King's College London London UK.
Learning health systems
|April 18, 2024
概括
这项研究引入了一种新的可计算的指南系统,用于管理慢性阻塞性肺病 (COPD) 和并发症,与专家肺病学家达成高度一致. 可解释的人工智能方法增强了临床决策支持系统 (CDSS).
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:
- 临床决策支持系统 (CDSS) 需要整合临床指南,以解决患者的并发症和潜在冲突.
- 现有的系统往往缺乏透明度和可解释性,阻碍了复杂患者病例的采用.
- 中等收入国家在部署电子健康记录 (EHR) 等先进医疗保健技术方面面临着独特的挑战.
研究的目的:
- 开发和评估一种非专有,基于标准的方法,用于部署具有可解释论证的可计算指南.
- 将该系统与塞尔维亚的商业电子健康系统相结合,重点关注慢性阻塞性肺病 (COPD) 与并发症.
- 通过模拟案例和专家审查来评估系统的准确性和可用性.
主要方法:
- 运用了本体学框架 (基于转变的医学建议 - TMR) 和基于假设的推理与偏好和目标 (ABA + G) 进行指导方针表示和冲突缓解.
- 使用基于HL7 FHIR和CDS Hooks的微服务架构实现远程EHR集成.
- 开发了一种用于治疗并发性心血管或慢性脏疾病的COPD的原型,并与20个模拟病例和5名肺科医生进行了评估.
主要成果:
- 肺科医生达成97%的同意与CDSS的COPD症状严重性评估和98%的同意与建议的护理计划.
- 专家们赞成该系统可解释的论证原则.
- 建议包括结合额外的并发症和根据专业知识定制解释水平.
结论:
- 一个本体模型提供了一种灵活的方法,提供可解释的AI和论证,用于管理长期条件.
- 开发的系统在现实世界临床环境中表现出高准确性和专家接受度.
- 需要通过将方法扩展到其他指导方针和多种并发症来进一步验证.
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