利用SpaCy从病历中清除敏感的PHI数据--一种可扩展的模型实现比较
Scrubbing Sensitive PHI Data from Medical Records made Easy by SpaCy -- A Scalable Model Implementation Comparisons
摘要：
临床记录的去识别化是一个非常重要的过程，它可以使用其中存在的大量信息。有很多技术可用于此，但没有一个方法实现评估了可伸缩性，这是一个重要的基准。我们评估了许多深度学习技术，如BiLSTM-CNN，IDCNN，CRF，BiLSTM-CRF，SpaCy等的性能和效率。我们建议，与其他已发布的模型相比，SpaCy模型实现用于从医疗记录中擦除敏感的PHI数据的性能良好且非常高效。
英文摘要：
De-identification of clinical records is an extremely important process which enables the use of the wealth of information present in them. There are a lot of techniques available for this but none of the method implementation has evaluated the scalability, which is an important benchmark. We evaluated numerous deep learning techniques such as BiLSTM-CNN, IDCNN, CRF, BiLSTM-CRF, SpaCy, etc. on both the performance and efficiency. We propose that the SpaCy model implementation for scrubbing sensitive PHI data from medical records is both well performing and extremely efficient compared to other published models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
