验证非小细胞肺癌的临床见解使用通用瘤自然语言处理模型
Rachel C Kenney1,2, Xiaoren Chen1, Kazuki Shintani1
1Optum Insight, Optum, Eden Prairie, MN.
JCO clinical cancer informatics
|September 4, 2024
概括
自然语言处理 (NLP) 准确地从医疗笔记中提取非小细胞肺癌 (NSCLC) 数据. 这种经过验证的模型显示出高精度和回忆,支持癌症研究和临床试验.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
背景情况:
- 有限的研究已经探索了自然语言处理 (NLP) 在非小细胞肺癌 (NSCLC) 的应用.
- 从非结构化的临床笔记中提取结构化数据对于癌症研究至关重要.
研究的目的:
- 验证NLP模型从自由文本医疗记录中提取NSCLC概念的能力.
- 将提取的概念转化为结构化,可解释的数据用于研究.
主要方法:
- 一个先前在广泛的瘤学队列上验证的NLP模型应用于200个NSCLC患者记录.
- 关键概念 (新生体,组织学,阶段,TNM,转移) 被手动抽象为黄金标准.
- 使用精度和回忆指标,NLP输出与黄金标准进行了比较.
主要成果:
- 对于所有提取的NSCLC概念,NLP模型实现了高精度和回忆.
- 评分包括:肺瘤 (100%, 100%),NSCLC组织学 (99%, 88%),阶段 (98.8%, 92%) 和转移部位 (97%, 89%).
- 高分数表明精确和全面的数据提取.
结论:
- 经过验证的NLP模型证明了高精度和回忆与现实世界的临床数据.
- 这种可通用的瘤NLP模型可靠地支持NSCLC研究和临床试验.
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