{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/111905"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/111905","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Information extraction with neural networks","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_html":"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_has_math":false,"creators":["Lee, Ji Young, Ph. D. Massachusetts Institute of Technology"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Peter Szolovits."],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017","date_published":"2017","updated_at":"2026-07-22T22:21:29Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["MIT theses are protected by copyright. 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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)."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph. D."]},{"key":"dc:title","label":"Title","values":["Information extraction with neural networks"]}]}],"canonical_facts":{"dc:contributor.advisor":["Peter Szolovits."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Lee, Ji Young, Ph. D. Massachusetts Institute of Technology"],"dc:date.accessioned":["2017-10-18T15:09:25Z"],"dc:date.available":["2017-10-18T15:09:25Z"],"dc:date.issued":["2017"],"dc:description":["Thesis: Ph. 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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)."],"dc:description.degree":["Ph. D."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/111905"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["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."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Information extraction with neural networks"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:29Z"}