在癌症治疗中使用低延迟神经网络从文本电子临床记录中实时命名实体识别.
Pir Noman Ahmad1, Muhammad Shahid Anwar1, Saleha Masood1
1IRC for Finance and Digital Economy, KFUPM Business School, King Fahd University of Petroleum and Minerals College of Sciences, Dhahran, Saudi Arabia.
Big data
|February 6, 2026
概括
本研究介绍了一个实时,低延迟的命名实体识别 (NER) 系统,用于医疗保健中的跨语言语音到文本. 它通过深度学习增强了从癌症治疗和传统中医记录中提取医疗数据的功能.
科学领域:
- 自然语言处理自然语言处理.
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 命名实体识别 (NER) 对于从非结构化文本中提取信息至关重要.
- 由于复杂的医学术语和准确性要求,NER的临床应用面临挑战.
- 越来越多的非结构化医疗数据需要有效的信息提取.
研究的目的:
- 开发一个实时,低延迟的NER系统,用于跨语言的语音到文本应用程序.
- 专注于癌症治疗和传统中医 (TCM) 临床记录.
- 从多语言口语医学内容中提取结构化的信息.
主要方法:
- 探索深度学习 (DL) 架构,优化用于低延迟神经处理.
- 评估基于DL的方法,用于多式联络环境中的NER.
- 建议采用半监督的方法,将TCM体和生物医学资源结合起来.
主要成果:
- 实时,低延迟NER的可行性已被证明,用于临床语音到文本.
- 通过 TCM 数据的半监督方法,提高了识别准确性.
- 提供了关于构建实用的临床信息提取系统的见解.
结论:
- 实时NER系统对于高效的医疗数据管理至关重要.
- 深度学习和半监督方法可以提高TCM等专业医疗领域的准确性.
- 开发的系统支持临床决策和信息检索.
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