大型语言模型提高了基于电子健康记录的预测在不同国家和编码系统的可转移性
Matthias Kirchler1,2,3, Matteo Ferro3, Veronica Lorenzini4
1Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, Potsdam, Germany.
medRxiv : the preprint server for health sciences
|February 20, 2025
概括
新的人工智能模型GRASP通过理解医疗代码,改善了不同医疗保健系统的疾病预测. 这提高了电子健康记录 (EHR) 模型的通用性,以获得更好的患者结果.
科学领域:
- 医疗保健中的人工智能
- 生物医学信息学 生物医学信息学
- 计算流行病学计算流行病学
背景情况:
- 医疗保健系统在医疗实践和报告标准方面存在差异.
- 这种异质性限制了从电子健康记录 (EHR) 数据开发的预测模型的可转移性.
- 现有的模型往往难以在不同的临床数据集中进行概括.
研究的目的:
- 引入GRASP,一种基于变压器的新型架构,以提高基于EHR的预测模型的通用性.
- 将医疗代码嵌入到统一的语义空间中,使用大语言模型 (LLM) 改进模型可转移性.
- 为了在异质的医疗保健系统中实现准确和可转移的疾病风险预测.
主要方法:
- 开发了GRASP,一种基于变压器的架构,利用LLMs用于医疗代码嵌入.
- 应用GRASP来预测来自英国生物银行,FinnGen和西奈山数据集的100多万个人的21种疾病和全因死亡率.
- 统一数据与OMOP共同数据模型进行初始培训和评估.
主要成果:
- 在FinnGen中评估时,GRASP的平均 ΔC指数比语言不知情模型高83%,在西奈山中高35%.
- 在62%的疾病中,与多基因风险得分的相关性明显更高.
- 在没有直接OMOP映射的情况下,通过非协调的ICD-10-CM代码预测疾病风险,保持了强大的性能.
结论:
- GRASP显著提高了基于EHR的预测模型的可通用性和可转移性.
- 医学代码的LLM驱动的语义嵌入克服了数据异质性的局限性.
- 格拉斯普提供了一个资源高效的解决方案,用于准确的,跨系统的疾病预测.
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