语言建模的实证评估,以从临床文本报告中确定癌症结果
Haitham A Elmarakeby1,2,3,4, Pavel S Trukhanov5, Vidal M Arroyo6
1Dana-Farber Cancer Institute, Boston, MA, USA. haithama_elmarakeby@dfci.harvard.edu.
BMC bioinformatics
|September 1, 2023
概括
这项研究评估了自然语言处理 (NLP) 模型,用于从成像报告中提取癌症结果. 虽然先进的模型显示出有前途,但当标记数据丰富时,更简单的方法的性能相对较高,突出显示了数据可用性对AI模型在癌症研究中的表现的影响.
科学领域:
- 在瘤学中使用人工智能
- 临床研究中的自然语言处理 (NLP)
- 针对癌症的机器学习 提取结果
背景情况:
- 标准的癌症登记处缺乏纵向结果数据 (例如治疗反应,进展).
- 从电子健康记录中手动提取结果是耗时和劳动密集的.
- NLP加速了结果注释,但需要大量的标记数据,对培训策略的评估有限.
研究的目的:
- 系统地评估NLP模型培训策略,从非结构化文本中提取癌症结果.
- 为了比较各种NLP模型的性能,包括那些基于大型语言模型和简单架构的模型.
- 评估训练数据大小和模型架构对结果提取准确性的影响.
主要方法:
- 评估了9个NLP模型,用于识别非小细胞肺癌成像报告中的癌症反应和进展.
- 在不同的条件下训练模型:样本大小,架构和语言模型预训练.
- 利用一个包含14218份报告的标记数据集和一个包含662579份报告的未标记数据集,预先训练一个定制的BERT模型 (DFCI-ImagingBERT).
主要成果:
- 在200多名患者中训练的基于DFCI-ImagingBERT的分类器在大多数实验中取得了最佳表现.
- 从先进的DFCI-ImagingBERT模型获得的性能增长与更简单的模型相比是微不足道的,例如"词包"或卷积神经网络.
- 模型性能受到训练样本大小和分类架构的影响.
结论:
- 对于癌症研究中的AI模型开发,具有有限的标记数据但充足的计算资源,大型语言模型提供了有效的零或少射击学习.
- 当计算资源有限但标记数据丰富时,更简单的机器学习架构可以为结果提取提供良好的性能.
- 在选择NLP模型和培训策略时,应考虑标记数据和计算资源的可用性.
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