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Medical health records and clinical summaries contain a vast amount ofimportant information in textual form that can help advancing research ontreatments, drugs and public health. However, the majority of these informationis not shared because they contain private information about patients, theirfamilies, or medical staff treating them. Regulations such as HIPPA in the US,PHIPPA in Canada and GDPR regulate the protection, processing and distributionof this information. In case this information is de-identified and personalinformation are replaced or redacted, they could be distributed to the researchcommunity. In this paper, we present MASK, a software package that is designedto perform the de-identification task. The software is able to perform namedentity recognition using some of the state-of-the-art techniques and then maskor redact recognized entities. The user is able to select named entityrecognition algorithm (currently implemented are two versions of CRF-basedtechniques and BiLSTM-based neural network with pre-trained GLoVe and ELMoembedding) and masking algorithm (e.g. shift dates, replace names/locations,totally redact entity).

MASK: A flexible framework to facilitate de-identification of clinical texts