专用词嵌入和命名实体的应用,以及对无监督自动临床编码问题的属性识别
Namrata Nath1, Sang-Heon Lee2, Ivan Lee2
1UniSA STEM, University of South Australia, GPO Box 2471, Adelaide, SA, 5001, Australia.
Computers in biology and medicine
|September 18, 2023
概括
这项研究引入了一种无监督的自动临床编码方法,消除了对标记数据的需求,并提高了可解释性. 该方法有效地从临床笔记中提取和编码信息,以实现准确的编码.
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.
- 机器学习 机器学习
背景情况:
- 临床文档产生了大量的非结构化文本数据.
- 手动临床编码是劳动密集型的,容易出现错误.
- 使用深度学习的自动临床编码显示出希望,但需要大量的注释数据集,缺乏可解释性.
研究的目的:
- 为自动化临床编码开发一种无监督和可解释的方法.
- 减少对手工注释的临床数据的依赖,以培训模型.
- 提高临床代码分配的效率和准确性.
主要方法:
- 使用命名实体和属性识别 (NEAR) 技术.
- 在临床文本中使用特定领域的词嵌入.
- 开发了用于代码分配的无监督学习方法.
主要成果:
- 提出的无监督方法成功地从临床笔记中收集信息.
- 该方法有效地编码临床信息进行自动编码.
- 该方法在代码分配中提供了可解释性,与传统的深度学习模型不同.
结论:
- 无监督的方法可以在没有标记数据的情况下实现准确的自动化临床编码.
- 临床编码中的可解释人工智能是可行的和有益的.
- 这种方法为有效和透明的临床代码分配提供了一个有希望的替代方案.
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