对比神经语言模型用于医学概念表示和患者轨迹预测
Alban Bornet1, Dimitrios Proios2, Anthony Yazdani2
1Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
Artificial intelligence in medicine
|March 14, 2025
概括
这项研究比较了word2vec,fastText和GloVe在电子健康记录中的医学概念表示. fastText在语义相似性方面表现出色,而word2vec和GloVe在预测患者结果方面更好.
科学领域:
- 计算语言学计算语言学
- 生物信息学是一种生物信息学.
- 医疗信息学 医疗信息学
背景情况:
- 医疗概念的有效表现对于分析电子健康记录 (EHR) 至关重要.
- 神经语言模型为从临床数据中提取医学概念表示提供了潜力.
- 在这个领域中,不同语言模型的比较性能和语义编码需要进一步研究.
研究的目的:
- 评估word2vec,fastText和GloVe用于创建医疗概念嵌入.
- 评估这些经验表征所捕获的语义意义.
- 为了比较它们在下游任务中的有效性,例如结果和轨迹预测.
主要方法:
- 训练有素的word2vec,fastText和GloVe对来自大型EHR数据集的患者轨迹进行了训练.
- 通过比较实证嵌入与生物医学术语来评估语义编码.
- 在预测患者结果 (停留时间,再入院,死亡率) 和未来医疗代码方面评估的表现.
主要成果:
- fastText嵌入显示了与诊断,程序和药物代码的理论集群的最高相似性 (0.88-0.92).
- 在预测结果方面,word2vec和GloVe的表现优于fastText (AUROC高达0.85).
- 在预测患者轨迹中的医疗代码方面,GloVe和fastText表现强.
结论:
- 副词信息对于学习医学概念表示至关重要.
- 全球嵌入矢量更适合于高级任务,如轨迹预测.
- 这些模型可以有效地从EHR数据中编码临床含义,突出显示机器学习对语义数据编码的潜力.
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