优化对患者门户信息数据集的文字嵌入,使用少量样本
Qingyuan Song1, Congning Ni1, Jeremy L Warner2
1Vanderbilt University.
Research square
|May 27, 2024
概括
一个新的模型,PK-word2vec,通过使用先前的知识来有效地处理患者门户消息,以增强词嵌入,即使数据有限. 这一进步改善了用于临床文本分析的自然语言处理.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 生物医学信息学 生物医学信息学
- 机器学习 机器学习
背景情况:
- 患者门户信息对于临床研究至关重要,但由于对特定疾病的数据有限,NLP存在挑战.
- 标准的词嵌入模型需要大量的数据集,这阻碍了它们在专门的临床信息机构中的应用.
- 从患者通信中提取有意义的信号需要先进的NLP技术.
研究的目的:
- 介绍PK-word2vec,这是一个新的word2vec适应,旨在在小规模消息数据集中进行有效的语义表示.
- 通过将从预先训练的模型中获得的先前知识纳入,提高文字嵌入模型的培训.
主要方法:
- PK-word2vec集成了现有嵌入模型中的类似的医疗和非医疗术语作为先前知识.
- 该模型应用于范德比尔特大学医学中心 (2004-2017) 的乳腺癌患者门户信息.
- 评估包括将PK-word2vec的词相似性结果与标准的word2vec进行比较,使用亚马逊机械土耳其工人和医学学生执行的任务.
主要成果:
- 该数据集包括来自1389个患者记录的137,554条消息.
- 在90%以上的评估任务中,PK-word2vec产生了比标准word2vec更相关的单词相似性 (p=0.01).
- 非专业人士和医学专家之间的评估者协议很高,这表明模型的性能强.
结论:
- PK-word2vec证明了从有限的患者门户信息数据中学习单词表示的有效性.
- 该模型代表了NLP应用在处理临床通信方面的重大进步.
- 这种方法可以从资源不足的生物医学文本数据中获得更深入的见解.
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