检测冗余的健康调查问题通过使用语言不可知双向编码器表示从变压器的句子嵌入:算法开发研究研究
Sunghoon Kang1, Hyewon Park1, Ricky Taira2
1College of Nursing, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea, 82 027408483.
JMIR medical informatics
|June 10, 2025
概括
这项研究开发了SBERT-LaBSE算法,以测量跨语言健康调查问题的语义相似性. 它有效地规范了个人生成的健康数据 (PGHD),提高了互操作性和研究潜力.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 数据标准化数据标准化
背景情况:
- 个人生成的健康数据 (PGHD) 在医疗保健和研究中越来越重要.
- 基于调查的PGHD的标准化对于可用性和互操作性至关重要.
- 像PROMIS和NIH CDE这样的现有方法是有价值的,但用于语义相似性的手动注释是劳动密集型的,并且难以扩展,特别是跨语言.
研究的目的:
- 计算英语和韩语健康调查问题之间的语义相似性.
- 促进基于调查的PGHD的标准化.
- 开发和评估跨语言语义相似性评估的算法.
主要方法:
- 从各种来源 (NIH CDE,PROMIS,韩国机构,出版物) 编制的多语言健康调查问题.
- 创建了一个由 1758 个问题对组成的数据集,与人类分配的相似性得分.
- 培训和评估了四个分类器:词袋,SBERT-BERT,SBERT-LaBSE和GPT-4o.
主要成果:
- 在评估跨语言问题的相似性方面,SBERT-LaBSE在评估跨语言问题的相似性方面取得了卓越的表现 (>0.99 AUC ROC和PR曲线).
- 斯伯特实验室有效地确定了英语和韩语问题之间的语义等价性.
- 虽然在对齐方面表现出色,但SBERT-LaBSE在微妙的细微差别和计算效率方面面临着挑战.
结论:
- 在健康调查中,SBERT-LaBSE算法为计算跨语言语义相似性提供了一个强大的工具.
- 它在标准化PGHD的传统方法和其他先进模型上显示了显著的优势.
- 未来的工作应该涉及更大的多语言数据集和得分规范化,以提高健康生活记录领域的一致性.
关键词:
贝尔特 (BERT) 公司实验室 实验室 实验室 实验室我们的PGHD是PGHD斯伯特·斯伯特 (SBERT SBERT) 是一个著名的作家.来自变压器的双向编码器表示互操作性互操作性互操作性的互操作性语言无关的BERT句子嵌入语句个人生成的健康数据语义上的相似性是语义上的相似性.来自变压器的句子双向编码器表示.更多相关视频
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