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Alistair Johnson. Lin, Jing,M. Eng.Massachusetts Institute of Technology. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2021-01-06T18:30:46Z 2021-01-06T18:30:46Z 2020 2020 https://hdl.handle.net/1721.1/129134 1227276464 Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020 Cataloged from student-submitted PDF of thesis. Includes bibliographical references (pages 71-73). Clinical notes contain rich information that is useful in medical research and investigation. However, clinical documents often contain explicit personal information that is protected by federal laws. Researchers are required to remove these personal identifiers before publicly release the notes, a process known as de-identification. In recent years, the healthcare community has initiated several competitions to expedite the development of automated de-identification systems. Notably, models built using recurrent neural networks achieved state-of-the-art performance on the de-identification task. Since the competition, new architectures based on transformers have been developed with excellent performance on general domain natural language processing tasks. Examples include BERT and RoBERTa. In this work, we evaluated de-identification using different choices of bidirectional transformer models and classifiers. Further, we developed a hybrid system that incorporates rule-based features into the bidirectional transformer model. Our results demonstrated state-of-the-art performance with an average 98.73% binary token F1 score, a 0.45% increase from current baseline models. by Jing Lin. M.Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 73 pages eng Massachusetts Institute of Technology MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. De-identification of free-text clinical notes Thesis application/pdf 2021-01-06T18:30:44Z Publication Master EECS unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="a107c1f4-ddf5-4ace-982b-0bb24682a753"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>De-identification of free-text clinical notes</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2020</PublicationDate> <Authors> <Author> <DisplayName>Lin, Jing,M. Eng.Massachusetts Institute of Technology.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Clinical notes contain rich information that is useful in medical research and investigation. However, clinical documents often contain explicit personal information that is protected by federal laws. Researchers are required to remove these personal identifiers before publicly release the notes, a process known as de-identification. In recent years, the healthcare community has initiated several competitions to expedite the development of automated de-identification systems. Notably, models built using recurrent neural networks achieved state-of-the-art performance on the de-identification task. Since the competition, new architectures based on transformers have been developed with excellent performance on general domain natural language processing tasks. Examples include BERT and RoBERTa. In this work, we evaluated de-identification using different choices of bidirectional transformer models and classifiers. Further, we developed a hybrid system that incorporates rule-based features into the bidirectional transformer model. Our results demonstrated state-of-the-art performance with an average 98.73% binary token F1 score, a 0.45% increase from current baseline models.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>