一个自我监督的框架用于电子健康记录中实验室数据的归算
Samuel P Heilbroner1, Curtis Carter1, David M Vidmar1
1Tempus AI, Inc., Chicago, IL, USA.
Communications medicine
|July 2, 2025
概括
实验室推算框架 (LIFE) 有效地使用自我监督学习来推算缺失的实验室值. 这增强了对现实世界的数据的使用,以获得更好的临床模型和患者的治疗结果.
科学领域:
- 生物医学信息学 生物医学信息学
- 医疗保健中的机器学习
- 电子健康记录 (EHR) 是一种电子健康记录.
背景情况:
- 电子健康记录中的实验室数据是有价值的,但由于缺少值而未得到充分利用.
- 目前的归算方法未能充分利用临床病史和有效扩展.
- 实验室值的高缺失限制了它们在诊断,治疗和研究中的使用.
研究的目的:
- 开发一个可扩展和有效的框架,用于在EHR中赋值缺失的实验室值.
- 为了利用完整的患者临床病史来更准确地归算实验室数据.
- 改善对医疗保健应用的真实世界数据 (RWD) 的利用.
主要方法:
- 开发了EHR (LIFE) 的实验室计算框架,这是一个自我监督的学习框架.
- 利用多头注意力架构共同建所有实验室数据的模型.
- 包含了额外的EHR变量 (诊断,药物) 用于临床上下文化.
主要成果:
- 与基线方法相比,LIFE 在25个实验室测试中,在23个实验室测试中实现了优异或同等的性能.
- 在下游的不良事件检测任务中表现得更好 (在9个案例中7个).
- 在超过100万名瘤患者的大规模数据集上进行了验证.
结论:
- 生命准确地估计了缺失的实验室值,提高了RWD在医疗保健中的利用.
- 该框架支持开发更好的临床模型和知情决策.
- 通过更完整和可靠的健康数据,改善患者治疗结果的潜力.
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