检查预训练的非识别变压器模型在叙述护理笔记上的概括性
Fangyi Chen1, Syed Mohtashim Abbas Bokhari1, Kenrick Cato2,3
1Department of Biomedical Informatics, Columbia University, New York, New York, United States.
Applied clinical informatics
|March 6, 2024
概括
变压器模型显示出分析护理笔记的希望,但它们的概括性各不相同. 退院摘要包含的个人保护健康信息 (PHI) 比护理笔记更多,影响了模型的性能.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理 (NLP) 是一种自然语言处理.
- 临床数据分析临床数据分析
背景情况:
- 叙事护理笔记充满了预测性患者护理信号,但隐私法规 (HIPAA) 限制了数据共享.
- 现有的NLP模型通常在出院摘要上进行评估,对它们对护理笔记的概括性进行了有限的研究.
- 了解不同临床文本上的模型性能对于推进信息学研究至关重要.
研究的目的:
- 评估预训练过的变压器模型在护理笔记上的通用性.
- 调查护理笔记和出院摘要之间个人保护健康信息 (PHI) 分布的差异.
- 为未来临床文本模型评估方案的设计提供信息.
主要方法:
- 两个变压器模型,RoBERTa和ClinicalBERT,在出院摘要上进行了微调,并在住院护理笔记上进行了评估.
- 性能与使用统计测试的基线模型进行了比较.
- 分析了文件类型之间的PHI分布模式的差异.
主要成果:
- 在对护理笔记进行测试时,RoBERTa实现了高性能 (在PHI类别中F1得分为0.887,在二进制PHI任务中为0.932).
- 与护理笔记相比,出院总结包含了更多的PHI实例和更多的PHI实例.
- 护理笔记显示PHI类型的多样性更大,包括个人生活细节.
结论:
- 预训练过的变压器模型可以应用于护理笔记,但它们的有效性受PHI分布的影响.
- 退院摘要和护理说明表现出明显的PHI特征,需要量身定制的模型评估.
- 结果指导了在临床笔记中选择算法和开发用于PHI检测的性能基准.
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