NSSC:一种神经符号AI系统,用于提高命名实体识别的准确性,并从瘤学临床笔记中链接
Álvaro García-Barragán1, Ahmad Sakor2,3, Maria-Esther Vidal4,5
1Center of Biomedical Technology, Universidad Politécnica de Madrid, Campus Montegancedo, Pozuelo de Alarcón, 28223, Madrid, Spain. alvaro.gbarragan@upm.es.
Medical & biological engineering & computing
|November 1, 2024
概括
我们开发了一个神经符号癌症系统 (NSSC),以准确识别和链接临床笔记中的癌症术语. 这种人工智能方法显著改善了用于癌症研究和患者护理的信息提取.
科学领域:
- 生物医学信息学 生物医学信息学
- 在瘤学中使用人工智能
背景情况:
- 在临床笔记中准确地识别和链接瘤实体对于推进癌症研究,患者护理和治疗优化至关重要.
- 当前的方法与临床叙述的复杂性和非结构性质作斗争.
研究的目的:
- 引入癌症神经象征系统 (NSSC),这是一个新的混合AI框架.
- 为了提高在临床注释中的瘤实体的命名实体识别 (NER) 和实体链接 (EL) 的准确性.
主要方法:
- NSSC将神经符号AI方法与NER和EL技术相结合.
- 该系统将非结构化的临床文本转化为结构化的术语,使用医学词汇,以统一医疗语言系统 (UMLS) 为例.
- 对来自乳腺癌患者的临床笔记数据集进行了评估.
主要成果:
- 与最先进的模型相比,NSSC在实体识别和链接准确性方面都取得了显著的改进.
- 与BioFalcon相比,实现了33%的改进,与scispaCy相比提高了58%.
- 结合大型语言模型 (LLM) 和符号推理的混合方法增强了瘤实体识别和互操作性.
结论:
- NSSC代表了从临床叙述中提取有意义信息的重大进步.
- 该系统可与现有的生物医学知识库进行无集成.
- NSSC为癌症研究,临床决策和个性化患者护理提供了有前途的应用.
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