开发和评估用于教学临床试验协议的技术术语提取工具
Naoki Nishimoto1, Ayako Yagahara2, Ken Sakushima3
1Data Science Center, Promotion Unit, HELIOS, Hokkaido University Hospital, Sapporo, Japan.
Studies in health technology and informatics
|August 8, 2025
概括
通过增强自然语言处理 (NLP) 工具,可以改进临床研究协议的计算机辅助教学. 对于医学和临床试验术语的NLP准确性显示出基于规则的改进的潜力,解决表达变化.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
背景情况:
- 临床研究协议教育需要准确提取医疗和试验相关术语.
- 现有的自然语言处理 (NLP) 工具和统一医疗语言系统 (UMLS) 用于术语提取.
研究的目的:
- 评估NLP工具在提取临床研究协议的术语时的准确性.
- 确定这些协议的计算机辅助教学需要改进的领域.
主要方法:
- 利用现有的NLP工具和UMLS作为术语提取字典.
- 执行了提取术语的手动验证.
- 分析了与手动验证不匹配的术语,以寻找潜在的基于规则的改进.
主要成果:
- 在671个独特条款和939个不匹配的总条款中,68.9%的独特条款和55.2%的总条款被认为是可改进的.
- 基于规则的方法显示了提高术语提取精度的潜力.
结论:
- 计算机辅助可以支持表达调整在教学临床研究协议.
- 以特定领域的术语来增强NLP工具,并解决表达式变异是提高准确性的关键.
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