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从非结构化的EHR中对肺癌的整体生存预测进行分层嵌入注意力

Domenico Paolo¹, Carlo Greco^2,3, Alessio Cortellini⁴

¹Unit of Computer Systems & Bioinformatics, Department of Engineering, University Campus Bio-Medico di Roma, Roma, Italy.

BMC medical informatics and decision making

|April 18, 2025

概括

这项研究使用先进的自然语言处理 (NLP) 来从电子健康记录 (EHR) 中提取丰富的信息,以预测癌症存活率. 该方法通过利用学习的表示来改善临床预测,优于传统的特征提取.

科学领域:

计算语言学计算语言学
医疗信息学医学信息学
在瘤学瘤学.

背景情况:

电子健康记录 (EHR) 包含非结构化数据,对临床见解有价值.
自然语言处理 (NLP) 和命名实体识别 (NER) 是从EHR中提取信息的关键.
现有的方法往往忽略了NLP模型的学习表征.

研究的目的:

探索基于变压器的NER模型在EHR中的学习表示的潜力.
开发一种增强的临床预测模型,使用这些表示用于非小细胞肺癌 (NSCLC) 的整体存活率 (OS).
证明拟议方法的可解释性.

主要方法:

从一个基于变压器的NER模型中提取的表示,应用于意大利的EHR.
使用层次关注机制的组合实体表示.
利用丰富的表示作为NSCLCOS的临床预测模型的输入.
通过专家协议对突出显示的EHR信息进行验证的解释性.

主要成果:

与传统方法相比,使用丰富的EHR表示的拟议方法在预测非小细胞肺癌 (NSCLC) 的整体存活率 (OS) 中显示了统计学上显著的改进.
层次的注意力机制为预测提供了对关键EHR信息的可解释的见解.
专家验证证实了注意力机制突出显示的信息的相关性.

结论:

从NLP模型中学习的表示提供了使用非结构化EHR数据进行临床预测的强大方法.
层次关注机制提高了预测准确性和模型可解释性.
这种方法为分析EHR和推进癌症研究提供了有价值的工具.

关键词:

注意力机制注意力机制肺癌是一种肺癌.尼尔·内尔 (NER NER NER) 是一个国家.对生存分析的分析.变压器变压器变压器没有结构的电子健康记录.

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