基于语言模型的患者嵌入使用电子健康记录促进表型,疾病预测和进展分析
Su Xian1, Monika E Grabowska2, Iftikhar J Kullo3
1Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA.
Research square
|October 14, 2024
概括
本研究引入了使用电子健康记录 (EHR) 和变压器模型的无监督患者嵌入方法. 它有效地描述了患者的异质性,并预测了未来的疾病事件,推进了个性化医疗.
科学领域:
- 生物医学信息学 生物医学信息学
- 机器学习 机器学习
- 计算生物学 计算生物学
背景情况:
- 目前电子健康记录 (EHR) 的二次使用严重依赖于领域专业知识.
- 在EHR数据中发现新的医疗模式需要强大的患者代表方法.
- 机器学习在EHR中的应用正在进步,但需要增强的患者嵌入技术.
研究的目的:
- 开发一种无监督的方法,将高维电子病历数据嵌入到患者层面.
- 在复杂疾病中描述患者异质性,并确定新的疾病模式.
- 通过先进的患者代表来改善疾病预测和促进个性化医疗.
主要方法:
- 利用基于变压器的架构与注意力机制,灵感来自语言模型.
- 治疗患者的诊断和程序代码作为词汇库,每个患者以句子的形式表示.
- 将该方法应用于来自电子医疗记录和GEnomics (eMERGE) 网络的大量数据集.
主要成果:
- 在预测未来疾病事件 (中位数AUROC = 0.87) 和批量表型 (中位数AUROC = 0.84) 中取得了出色的表现.
- 揭示了复杂疾病的异质并发症模式和独特的纵向疾病轨迹.
- 外部验证证实了强大的模型性能,表型化和预测的中位数AUROC分别为0.83和0.84.
结论:
- 电子健康记录中的代表性学习显著提高了患者的表征和结果预测.
- 开发的患者嵌入方法可以发现疾病亚型和死亡率的变化.
- 这种方法在推进疾病预测和个性化医疗方面具有巨大的潜力.
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