对结构化电子健康记录的基础模型进行系统审查
Lin Lawrence Guo1, Santiago Eduardo Arciniegas1, Adam Paul Yan1,2
1Child Health Evaluative Sciences, Research Institute, The Hospital for Sick Children, Toronto, Canada.
概括
使用电子健康记录 (EHR) 的基础模型正在增长,转向变压器架构和生成性使用. 然而,碎片化的报告阻碍了为这些临床预测模型建立最佳实践.
科学领域:
- 医疗保健中的人工智能
- 机器学习用于临床预测
- 医学中的基础模型.
背景情况:
- 在结构化电子健康记录 (EHR) 数据上进行预训的基础模型提供了增强预测性能,样本效率和对数据分布转移的弹性.
- 然而,模型设计,规模和现实应用的关键方面仍未得到充分探索.
- 了解这些趋势对于在临床环境中推进AI至关重要.
研究的目的:
- 在结构化EHR数据上预训练的基础模型的特征.
- 检查应用,规模,建筑和设计中的时间趋势.
- 评估相关出版物中方法详细报告的程度.
主要方法:
- 从2018年到2025年10月,在MEDLINE和Embase进行了系统的文献搜索.
- 研究集中在基础模型上,这些模型在结构化的电子健康记录数据上进行了预训,并使用自主监督学习来执行临床预测任务.
- 数据抽象和特征总结是按出版年分层进行的.
主要成果:
- 包括53项研究,显示出版物的时间增加.
- 模型架构越来越多地使用具有较长上下文窗口的变压器,应用程序转向生成或混合用途.
- 方法选择,规模/计算报告和对外部数据集的可转移性评估高度异质,没有描述临床部署的研究.
结论:
- 电子健康数据基础模型正在激增,显著转向基于变压器和生成的方法.
- 分断的方法报告和缺乏确定的最佳实践对设计和实施构成挑战.
- 需要进一步的研究来澄清性能和稳定性最佳设计选择,提高报告透明度,并验证患者结果的临床实用性.
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