从语言嵌入式EHR中学习肺结节诊断的纵向面具表示
medRxiv : the preprint server for health sciences
|May 19, 2025
概括
蒙面表示学习 (MRL) 提高了使用电子健康记录 (EHR) 的肺结节诊断. 这种方法通过在多种临床模式中建模纵向EHR数据来提高诊断准确性.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 临床数据科学 临床数据科学
背景情况:
- 电子健康记录 (EHR) 包含有价值的纵向临床数据,但由于噪音和抽象,对肺结节的诊断具有挑战性.
- 标记数据的稀缺性和模型过度匹配是使用EHR开发肺结节的准确诊断分类器的重大障碍.
研究的目的:
- 通过对纵向EHR数据进行建模,研究蒙面表示学习 (MRL) 作为一种改进肺结节诊断的方法.
- 评估MRL在整合多种EHR模式中的有效性,包括临床条件,程序和药物.
主要方法:
- 利用网络规模的文本嵌入模型将EHR事件流编码为语义嵌入序列.
- 预先训练了一台双向变压器,使用在大量一般肺部疾病中使用时间编码条件的MRL.
- 在诊断出肺结节的队列上评估了MRL微调模型,将其准确性与标准监督模型进行比较.
主要成果:
- 与监督模型 (0.768 AUC) 相比,MRL微调模型在整合临床条件,程序和药物时实现了更高的诊断准确度 (0.781 AUC).
- 通过将MRL应用于纵向EHR数据,显著改善了肺结节诊断的准确性.
- 该研究强调了语言嵌入的MRL在加强下游临床分类任务方面的有效性.
结论:
- 蒙面表示学习 (MRL) 是一种有前途的策略,可以从纵向EHR数据中改进肺结节的诊断.
- 通过语言嵌入的MRL集成多种EHR模式,可以提高模型性能,并为临床数据分析提供潜在的进步.
- 这种方法可以帮助克服与临床分类数据稀缺性和模型过拟合相关的挑战.
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