从患有淋巴瘤恶性病的退伍军人笔记中提取临床信息:自然语言处理研究
Lu He1, Matthew R Moldenhauer2, Kai Zheng3,4
1Zilber College of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, United States.
这项研究开发了一种临床自然语言处理 (cNLP) 管道,用于退伍军人的淋巴细胞恶性瘤. 虽然cNLP系统通常有效,但在确定初级诊断和物质使用方面存在种族差异.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 在瘤学瘤学.
背景情况:
- 临床自然语言处理 (cNLP) 对于从临床笔记中提取数据至关重要,但由于数据稀缺性和文档复杂性,其对淋巴细胞恶性瘤等罕见疾病的应用受到限制.
- 现有的cNLP模型可能会延续临床数据或开发过程中存在的偏见,可能导致性能差异.
研究的目的:
- 开发和验证cNLP管道,从患有淋巴细胞恶性瘤的退伍军人中提取临床信息.
- 评估管道在确定关键临床实体方面的表现,包括性能状态,分期,诊断,物质使用和军事环境暴露.
主要方法:
- 创建了一个基于规则的cNLP管道,结合了领域专业知识.
- 管道的表现被评估在两个不同的退伍军人队伍的临床笔记上:非西班牙裔白人和非西班牙裔黑人退伍军人.
主要成果:
- cNLP管道表现出有前途的业绩,特别是对于表现状等记录良好的实体.
- 在初级诊断检测的虚假阳性和虚假阴性率以及在物质使用识别的虚假阴性率中发现了显著的种族关联.
结论:
- 开发的cNLP系统在患有淋巴细胞恶性瘤的退伍军人中的大多数临床实体中显示出令人满意和可比的性能.
- 未来的研究应该专注于完善管道,以应对罕见癌症数据的挑战,并减轻与种族有关的绩效偏见.
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