  Electronic Health Records (EHRs) have become the primary form of medical
data-keeping across the United States. Federal law restricts the sharing of any
EHR data that contains protected health information (PHI). De-identification,
the process of identifying and removing all PHI, is crucial for making EHR data
publicly available for scientific research. This project explores several deep
learning-based named entity recognition (NER) methods to determine which
method(s) perform better on the de-identification task. We trained and tested
our models on the i2b2 training dataset, and qualitatively assessed their
performance using EHR data collected from a local hospital. We found that 1)
BiLSTM-CRF represents the best-performing encoder/decoder combination, 2)
character-embeddings and CRFs tend to improve precision at the price of recall,
and 3) transformers alone under-perform as context encoders. Future work
focused on structuring medical text may improve the extraction of semantic and
syntactic information for the purposes of EHR de-identification.
