De-identification of Unstructured Clinical Texts from Sequence to Sequence Perspective

• URL: http://arxiv.org/abs/2108.07971v1
• Date: Wed, 18 Aug 2021 04:48:58 GMT
• Title: De-identification of Unstructured Clinical Texts from Sequence to Sequence Perspective
• Authors: Md Monowar Anjum, Noman Mohammed, Xiaoqian Jiang
• Abstract summary: We formulate the de-identification problem as a sequence to sequence learning problem instead of a token classification problem. Early experimentation of our proposed approach achieved 98.91% recall rate on i2b2 dataset.
• Score: 8.615499133294097
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this work, we propose a novel problem formulation for de-identification of unstructured clinical text. We formulate the de-identification problem as a sequence to sequence learning problem instead of a token classification problem. Our approach is inspired by the recent state-of -the-art performance of sequence to sequence learning models for named entity recognition. Early experimentation of our proposed approach achieved 98.91% recall rate on i2b2 dataset. This performance is comparable to current state-of-the-art models for unstructured clinical text de-identification.

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