积极学习管道用于识别CDSS本体论的候选术语
Xia Jing1, Rohan Goli2, Keerthana Komatineni2
1Department of Public Health Sciences, College of Behavioral, Social, and Health Sciences, Clemson University, Clemson, SC, USA.
Studies in health technology and informatics
|August 23, 2024
概括
本研究引入了一种主动学习方法,用于自动识别生物医学本体学的术语,提高效率并帮助长期维护.
科学领域:
- 生物医学信息学 生物医学信息学
- 卫生信息技术 信息技术
- 计算语言学 计算语言学
背景情况:
- 实体学对于健康信息和IT的互操作性至关重要.
- 由人类领域专家 (HDE) 手动构建本体学是耗时的.
- 现有的方法在可扩展性和长期维护方面面临挑战.
研究的目的:
- 探索一种主动学习方法,用于自动化的本体学术语识别.
- 为深度学习模型培训集成自动化术语识别与手动验证.
- 提高本体论开发和维护的效率和可持续性.
主要方法:
- 开发了一个积极的学习管道,以从生物医学出版物中识别候选术语.
- 纳入了候选术语的手动验证步骤.
- 利用深度学习模型进行术语分类和本体学改进.
主要成果:
- 证明了对本体学术语提取的积极学习方法的可行性.
- 介绍了初步结果,显示了自动化本体结构构建过程部分的潜力.
- 强调了这种方法对手工HDE努力的补充性.
结论:
- 积极学习提供了一个有前途的策略来加速本体论的发展.
- 这种方法可以显著减少手动术语识别的负担.
- 拟议的管道支持生物医学本体学的高效长期维护.
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