基于快速规则的NER在SpaCy中用于CheXpert的14个类别的胸部放射学报告
概括
这项研究引入了一个自然语言处理工具,自动标记胸部X射线报告,大大加快了人工智能 (AI) 模型培训数据的创建速度.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 胸部X射线的解释是耗时的,需要人工智能辅助系统.
- 高质量的标记数据对于训练用于医学成像的准确AI模型至关重要.
- 胸部X射线报告的手动标签是人工智能开发中的瓶.
研究的目的:
- 开发一种有效的方法来使用NLP和命名实体识别 (NER) 来标记胸部X射线报告.
- 创建一个基于规则的NER工具,从临床文本中提取诊断信息.
- 改进用于人工智能医疗图像分析的培训数据集的生成.
主要方法:
- 开发了一个基于规则的NER工具,使用SpaCy来识别CheXpert的14个诊断类别.
- 利用美国国家医学图书馆的Open-i数据集来测试NER工具.
- 将性能和计算时间与CheXpert Labeler进行了比较.
主要成果:
- SpaCy NER工具在从胸部X射线报告中标记医疗术语方面取得了很高的准确性.
- 开发的工具处理的数据是CheXpert Labeler的计算时间的六分之一.
- 与现有方法相比,在识别诊断类别方面证明了同等的准确性.
结论:
- 基于NLP的NER方法显著提高了标记胸部X射线报告的效率.
- 这种方法可以减少人工智能培训数据集的人为错误和时间.
- 自动标签有望提高AI辅助医疗图像分析的准确性和减少负担.
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