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Peter Szolovits. Lee, Ji Young, Ph. D. 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 2017-10-18T15:09:25Z 2017-10-18T15:09:25Z 2017 2017 http://hdl.handle.net/1721.1/111905 1005139501 Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. Cataloged from PDF version of thesis. Includes bibliographical references (pages 85-97). Electronic health records (EHRs) have been widely adopted, and are a gold mine for clinical research. However, EHRs, especially their text components, remain largely unexplored due to the fact that they must be de-identified prior to any medical investigation. Existing systems for de-identification rely on manual rules or features, which are time-consuming to develop and fine-tune for new datasets. In this thesis, we propose the first de-identification system based on artificial neural networks (ANNs), which achieves state-of-the-art results without any human-engineered features. The ANN architecture is extended to incorporate features, further improving the de-identification performance. Under practical considerations, we explore transfer learning to take advantage of large annotated dataset to improve the performance on datasets with limited number of annotations. The ANN-based system is publicly released as an easy-to-use software package for general purpose named-entity recognition as well as de-identification. Finally, we present an ANN architecture for relation extraction, which ranked first in the SemEval-2017 task 10 (ScienceIE) for relation extraction in scientific articles (subtask C). by Ji Young Lee. Ph.D. 97 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Information extraction with neural networks Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="6a5eac5b-3707-436d-9b30-5559a0be67d0"> <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>Information extraction with neural networks</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2017</PublicationDate> <Authors> <Author> <DisplayName>Lee, Ji Young, Ph. D. 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>Electronic health records (EHRs) have been widely adopted, and are a gold mine for clinical research. However, EHRs, especially their text components, remain largely unexplored due to the fact that they must be de-identified prior to any medical investigation. Existing systems for de-identification rely on manual rules or features, which are time-consuming to develop and fine-tune for new datasets. In this thesis, we propose the first de-identification system based on artificial neural networks (ANNs), which achieves state-of-the-art results without any human-engineered features. The ANN architecture is extended to incorporate features, further improving the de-identification performance. Under practical considerations, we explore transfer learning to take advantage of large annotated dataset to improve the performance on datasets with limited number of annotations. The ANN-based system is publicly released as an easy-to-use software package for general purpose named-entity recognition as well as de-identification. Finally, we present an ANN architecture for relation extraction, which ranked first in the SemEval-2017 task 10 (ScienceIE) for relation extraction in scientific articles (subtask C).</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>