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

• URL: http://arxiv.org/abs/2103.13546v1
• Date: Thu, 25 Mar 2021 01:26:58 GMT
• Title: Benchmarking Modern Named Entity Recognition Techniques for Free-text Health Record De-identification
• Authors: Abdullah Ahmed, Adeel Abbasi, Carsten Eickhoff
• Abstract summary: Federal law restricts the sharing of any EHR data that contains protected health information (PHI) 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.
• Score: 6.026640792312181
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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