西北大学的资源和教育发展举措,以推进跨学习健康系统的协作人工智能
Yuan Luo1,2,3, Chengsheng Mao1,2,3, Lazaro N Sanchez-Pinto3,4,5
1Northwestern University Clinical and Translational Sciences Institute Chicago Illinois USA.
Learning health systems
|July 22, 2024
概括
我们开发了数据,工具和教育,以培养在医疗保健领域的协作人工智能 (AI) 多学科工作人员. 这个倡议促进了合作,推进了人工智能.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 人工智能在医学中的应用
- 医疗保健中的机器学习
背景情况:
- 人工智能 (AI) 在医疗保健领域的快速发展凸显了需要一个熟练的,多学科的劳动力,能够在学习医疗系统中有效协作.
- 最大限度地提高团队协同效应对于成功的医疗保健协作AI计划至关重要.
研究的目的:
- 开发数据,工具和教育资源,培养下一代多学科的医疗保健协作AI工作人员.
- 促进临床医生和人工智能科学家之间的有效合作.
主要方法:
- 开发了大量的自然语言处理 (NLP) 管道,从临床笔记中提取结构化信息,并将其存储在共同的数据模型中.
- 创建多式人工智能/机器学习 (ML) 工具和教程,用于分析各种医疗数据.
- 建立了跨学科培训的协作环境.
主要成果:
- 民主化对非结构化的健康信息,AI/ML工具和协作教育资源的访问.
- 实现了跨临床专业的众多研究,在2017年至2022年期间产生了68篇同行评审出版物.
- 在2022年将跨学科努力机构化为医疗保健协作AI中心.
结论:
- 在医疗保健领域的协作人工智能计划已经产生了宝贵的教育和实践资源.
- 使临床医生,科学家和管理人员能够有效地在研究和实践中应用AI方法.
- 通过加强协作和人工智能采用,推进机构级学习健康系统.
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