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基于领域知识融合的临床研究文本总结方法

Shiwei Jiang¹, Qingxiao Zheng², Taiyong Li³

¹Blockchain Industrial College (CUIT Shuangliu Industrial College), Chengdu University of Information Technology, Chengdu 610225, China. Electronic address: https://twitter.com/zhizhid.

Journal of biomedical informatics

|June 10, 2024

概括

这项研究介绍了DKGE-PEGASUS,这是一种结合领域知识的临床研究文本总结的新方法. 该方法提高了生物医学文本的理解,并通过识别关键元素来产生更高质量的摘要.

科学领域:

生物医学信息学生物医学信息学
自然语言处理自然语言处理.
临床研究临床研究

背景情况:

临床研究产生了大量的文本数据.
有效地总结这些数据对于知识传播至关重要.
现有的总结模型可能难以捕捉特定领域的细微差别.

研究的目的:

将领域知识,特别是 PICO (人口,干预,比较,结果) 元素整合到临床研究文本总结中.
提高人工智能模型对生物医学文本的理解.
为最终用户提升生成摘要的质量.

主要方法:

一种新的方法,DKGE-PEGASUS (Domain-Knowledge and Graph Convolutional Enhanced PEGASUS) 已经开发出来. 这是一种新的方法,DKGE-PEGASUS (Domain-Knowledge and Graph Convolutional Enhanced PEGASUS) 已经开发出来.
它包含一个PICO标签预测模块和图形卷积神经网络 (GCN) 来丰富文本信息.
预先训练的总结模型的编码器得到了域知识和GCN特征的加强.

主要成果:

在PubMed和CDSR数据集上的实验显示出显著的有效性.
获得了 42.64 (PubMed) 和 38.57 (CDSR) 的 Rouge-1 分数.
与基线模型相比,证明了优越的总结质量.

结论:

提出的DKGE-PEGASUS方法有效地识别了临床研究文本中的关键元素.
这种方法导致生成更高质量的摘要.
整合领域知识显著提高了临床文本总结性能.

关键词:

自动文本总结自动化文本总结领域知识融合领域知识融合图表卷积神经网络的图.这就是PICO知识的基础.预先训练有素的模型.

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