De-Identification of Clinical Notes Using Contextualized Language Models and a Token Classifier

• Joaquim Santos University of Évora http://orcid.org/0000-0002-0581-4092
• Henrique D. P. dos Santos noharm.ai https://orcid.org/0000-0002-2410-3536
• Fábio Tabalipa Memed https://orcid.org/0000-0002-2060-953X
• Renata Vieira University of Évora http://orcid.org/0000-0003-2449-5477

Resumo

The de-identification of clinical notes is crucial for the reuse of electronic clinical data and is a common Named Entity Recognition (NER) task. Neural language models provide a great improvement in Natural Language Processing (NLP) tasks, such as NER, when they are integrated with neural network methods. This paper evaluates the use of current state-of-the-art deep learning methods (Bi-LSTM-CRF) in the task of identifying patient names in clinical notes, for de-identification purposes. We used two corpora and three language models to evaluate which combination delivers the best performance. In our experiments, the specific corpus for the de-identification of clinical notes and a contextualized embedding with word embeddings achieved the best result: an F-measure of 0.94.

Palavras-chave: De-identification, Clinical notes, Language model, Token classifier