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arXiv · 2108.07971

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

Md Monowar Anjum·Noman Mohammed·Xiaoqian Jiang
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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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Md Monowar Anjum, Noman Mohammed, Xiaoqian Jiang. 2021-08-18. De-identification of Unstructured Clinical Texts from Sequence to Sequence Perspective. https://doi.org/10.1145/3460120.3485354

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