人工智能辅助生物医学文献知识综合,以支持精准瘤学的决策
Ting He1,2, Kory Kreimeyer1,2, Mimi Najjar1,3
1Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD.
概括
像BioBERT这样的自然语言处理 (NLP) 工具通过从生物医学文本中提取关键信息来提高精确瘤学. 这些人工智能辅助的方法增强了针对性治疗的知识检索.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 在瘤学瘤学.
背景情况:
- 有效的向疗法依赖于分析瘤分子形状并将其与临床表型相匹配.
- 从广泛的生物医学文献,注册表和数据库中获取和综合知识是必不可少的.
研究的目的:
- 评估自然语言处理 (NLP) 在生物医学领域的知识检索和合成方法.
- 评估NLP工具在支持精密瘤学的命名实体识别 (NER) 和关系提取 (RE) 中的性能.
主要方法:
- 测试了PubTator 3.0,来自变压器的双向编码器表示 (BERT) 和大型语言模型 (LLM).
- 使用生物医学文本对命名实体识别 (NER) 和关系提取 (RE) 任务的评估性能.
主要成果:
- 在PubTator 3.0中,NER获得了最高的F1分数 (0.93分).
- 在RE中,BioBERT表现优越 (F1得分为0.79),识别了大多数实体和关系.
- 生物BERT在一个特定的用例中表现出色,识别了几乎所有实体提及和大多数关系.
结论:
- 人工智能辅助的NLP方法,特别是BioBERT,显著增强了从生物医学文献中获取和合成知识的方法.
- 这些发现支持整合人工智能工具,以促进精密瘤学的知情决策.
- NLP模型在加速将分子瘤形状与临床表型相匹配以进行向治疗选择方面表现出前途.
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