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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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使用大型语言模型在临床笔记中增强表型识别:PhenoBCBERT和PhenoGPT.

Jingye Yang^1,2, Cong Liu³, Wendy Deng¹

¹Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.

|November 21, 2023

概括

新的人工智能模型PhenoBCBERT和PhenoGPT通过扩展人类表现型本体学 (HPO) 术语,帮助疾病分析,改善了临床笔记中遗传疾病表型的识别.

科学领域:

计算生物学是一种计算生物学.
医疗信息学医学信息学
遗传学遗传学是一个

关键词:

人类现象型本体学临床注意事项临床注意事项电子健康记录是电子健康记录.命名实体的认可命名实体的认可变压器变压器变压器变压器

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背景情况:

人类表型本体学 (HPO) 为遗传疾病提供了标准化的表型词汇.
现有的表型识别工具在全面的术语捕获方面扎,限制了临床笔记分析.

结论:

基于LLM的模型显著提高了临床文本中的自动化表型检测.
改进的表型识别有助于更强大的下游分析人类疾病.
这些模型为推进遗传疾病研究和临床应用提供了强大的工具.