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In this work, we propose a novel problem formulation for de-identification ofunstructured clinical text. We formulate the de-identification problem as asequence to sequence learning problem instead of a token classificationproblem. Our approach is inspired by the recent state-of -the-art performanceof sequence to sequence learning models for named entity recognition. Earlyexperimentation of our proposed approach achieved 98.91% recall rate on i2b2dataset. This performance is comparable to current state-of-the-art models forunstructured clinical text de-identification.

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