一个混合自然语言处理平台,用于多站点RWD研究.
Kento Sugimoto1, Yasushi Matsumura1,2, Shoya Wada1,3
1Department of Medical Informatics, Osaka University Graduate School of Medicine, Osaka, Japan.
Studies in health technology and informatics
|August 8, 2025
概括
本研究介绍了一种混合平台,用于使用自然语言处理 (NLP) 进行高效的真实世界数据 (RWD) 分析. 它显著加快了处理速度,同时在多个地点的研究中保护了患者的隐私.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 数据科学数据科学数据科学
背景情况:
- 电子医疗记录中的真实世界数据 (RWD) 对医疗保健研究至关重要.
- 整合非结构化的自由文本临床数据带来了重大挑战.
- 高的计算成本和隐私问题阻碍了自然语言处理 (NLP) 在多站点RWD研究中的实施.
研究的目的:
- 提出一个混合平台,以从自由文本临床数据中提取高效且保护隐私的信息.
- 促进多个机构在RWD研究中的有效数据整合.
- 在医疗保健研究中解决与NLP相关的计算和隐私挑战.
主要方法:
- 开发一个混合平台,将集中的NLP处理与隐私保护相结合.
- 对比实验评估混合平台与完全分布式方法的比较.
- 使用500份样本报告进行绩效评估.
主要成果:
- 中央NLP处理服务器表现出明显优异的性能,在0.12秒内处理报告,而现场服务器的时间为64.23秒.
- 中央服务器显示,无论报告长度如何,处理时间的增长是最小的,并且始终保持一致,这表明了效率和可扩展性.
- 混合平台有效地解决了隐私和数据治理问题.
结论:
- 开发的混合平台提高了在RWD研究中NLP任务的计算效率.
- 该平台成功地解决了多站点研究中的隐私和数据治理挑战.
- 这种方法有助于有效地从各个机构的自由文本临床数据中提取信息.
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