Scrubbing Sensitive PHI Data from Medical Records made Easy by SpaCy -- A Scalable Model Implementation Comparisons
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.
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