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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.

Patterns (New York, N.Y.)

|January 24, 2024

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) 术语来改善临床笔记中的遗传疾病表型的识别,帮助疾病研究.

关键词:

贝尔特 (BERT) 公司在 GPT 中,GPT 必须是 GPT.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的变压器是一个变压器.

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科学领域:

计算生物学计算生物学
医疗信息学医疗信息学
遗传学是一个遗传学.

背景情况:

人类表型本体学 (HPO) 为遗传疾病中的表型提供了一个标准化的词汇.
现有的表型识别工具通常在捕捉全谱表型异常方面存在局限性.
传统的启发式或基于规则的方法与新或复杂的现象型描述作斗争.

研究的目的:

在临床笔记中开发自动化表型识别的先进模型.
使用大型语言模型扩大人类现象型本体学 (HPO) 术语的词汇.
改善已知和以前未被描述的表型概念的检测.

主要方法:

开发了两个新型模型:PhenoBCBERT (基于BERT) 和PhenoGPT (基于GPT).
利用大型语言模型自动检测和从临床文本中扩展HPO术语.
与现有工具 (如PhenoTagger) 进行比较分析.
通过生物医学文献的案例研究进行评估,并评估模型架构和准确性.

主要成果:

与现有的工具相比,PhenoBCBERT和PhenoGPT确定了更广泛的表型概念.
这些模型成功地检测出了目前HPO词汇中不存在的表型.
在涉及生物医学文献的案例研究中表现强.
对比分析突出了基于BERT和GPT架构的优缺点.

结论:

开发的模型显著增强了从临床文本中自动化表型检测.
这些进展提高了在遗传疾病中表型识别的准确性和全面性.
这些模型为人类疾病研究和理解提供了更强大的下游分析.