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Electronic Health Records (EHRs) have become the primary form of medicaldata-keeping across the United States. Federal law restricts the sharing of anyEHR data that contains protected health information (PHI). De-identification,the process of identifying and removing all PHI, is crucial for making EHR datapublicly available for scientific research. This project explores several deeplearning-based named entity recognition (NER) methods to determine whichmethod(s) perform better on the de-identification task. We trained and testedour models on the i2b2 training dataset, and qualitatively assessed theirperformance 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 workfocused on structuring medical text may improve the extraction of semantic andsyntactic information for the purposes of EHR de-identification.

Benchmarking Modern Named Entity Recognition Techniques for Free-text Health Record De-identification