将微调变压器语言模型推广到新的临床环境中
Kevin Xie1,2, Samuel W Terman3, Ryan S Gallagher2,4
1Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
JAMIA open
|August 21, 2023
概括
这项研究表明,用于治疗的自然语言处理模型在机构之间表现良好,但需要对非专家笔记进行调整. 发作频率和最后一次发作提取日期有效地概括为多中心研究.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 治疗的护理
背景情况:
- 开发了一个自然语言处理 (NLP) 管道,从专家的临床笔记中提取发作自由度,发作频率和最后一次发作日期.
- 了解这种NLP方法在不同临床环境中的通用性,对于更广泛的应用至关重要.
研究的目的:
- 评估现有的NLP管道在非专家和外部机构的临床笔记上的性能和通用性.
- 在新的护理环境中确定影响模型表现的因素.
主要方法:
- 该NLP管道的测试是在未经专家培训的神经病学家和非神经病学家的未见的笔记上进行的.
- 该管道还根据来自外部医疗中心的病专家笔记进行了评估,并进行了轻微的预处理适应.
- 通过测量文档之间的物理和语义相似性来分析性能差异.
主要成果:
- 在对非专业人士或外部机构的笔记应用时,扣押自由度的分类协议减少了至少0.12.
- 文本重叠与黄金标准注释减少了至少0.11F1在内部注释,当有一个答案存在.
- 对外部专家笔记的概括显示,性能损失最小 (≤0.02协议),特别是在发作频率和最后一次发作日期方面.
结论:
- 对于症护理的NLP模型性能与非专家笔记下降,突出了语法和语义变化的影响.
- 对外部专家笔记进行有效的概括,并进行轻微的预处理,支持多中心的合作,以获取发作频率和最后一次发作提取日期.
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