{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/76815"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/76815","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"A de-identifier for electronic medical records based on a heterogeneous feature set","abstract":"In this thesis, I describe our effort to build an extended and specialized Named Entity Recognizer (NER) to detect instances of Protected Health Information (PHI) in electronic medical records (A de-identifier). The de-identifier was built by creating a comprehensive set of features formed by combining features from the most successful named entity recognizers and de-identifiers and using them in a SVM classifier. We show that the benefit from having an inclusive set of features outweighs the harm from the very large dimensionality of the resulting classification problem. We also show that our classifier does not over-fit the training data. We test whether this approach is more effective than using the NERs separately and combining the results using a committee voting procedure. Finally, we show that our system achieves a precision of up to 1.00, a recall of up to 0.97, and an f-measure of up to 0.98 on a variety of corpora.","abstract_html":"In this thesis, I describe our effort to build an extended and specialized Named Entity Recognizer (NER) to detect instances of Protected Health Information (PHI) in electronic medical records (A de-identifier). The de-identifier was built by creating a comprehensive set of features formed by combining features from the most successful named entity recognizers and de-identifiers and using them in a SVM classifier. We show that the benefit from having an inclusive set of features outweighs the harm from the very large dimensionality of the resulting classification problem. We also show that our classifier does not over-fit the training data. We test whether this approach is more effective than using the NERs separately and combining the results using a committee voting procedure. Finally, we show that our system achieves a precision of up to 1.00, a recall of up to 0.97, and an f-measure of up to 0.98 on a variety of corpora.","abstract_has_math":false,"creators":["Tafvizi, Arya (Tafvizi Zavareh)"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Peter Szolovits and Ozlem Uzuner."],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011","date_published":"2011","updated_at":"2026-07-22T22:21:19Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/76815","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Peter Szolovits and Ozlem Uzuner."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."]},{"key":"dc:creator","label":"Author","values":["Tafvizi, Arya (Tafvizi Zavareh)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2013-02-13T21:23:53Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2013-02-13T21:23:53Z"]},{"key":"dc:date.issued","label":"Date","values":["2011"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical Engineering and Computer Science."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/76815"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.","This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from PDF version of thesis.","Includes bibliographical references (p. 73-74)."]},{"key":"dc:description.abstract","label":"Abstract","values":["In this thesis, I describe our effort to build an extended and specialized Named Entity Recognizer (NER) to detect instances of Protected Health Information (PHI) in electronic medical records (A de-identifier). The de-identifier was built by creating a comprehensive set of features formed by combining features from the most successful named entity recognizers and de-identifiers and using them in a SVM classifier. We show that the benefit from having an inclusive set of features outweighs the harm from the very large dimensionality of the resulting classification problem. We also show that our classifier does not over-fit the training data. We test whether this approach is more effective than using the NERs separately and combining the results using a committee voting procedure. Finally, we show that our system achieves a precision of up to 1.00, a recall of up to 0.97, and an f-measure of up to 0.98 on a variety of corpora."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["A de-identifier for electronic medical records based on a heterogeneous feature set"]}]}],"canonical_facts":{"dc:contributor.advisor":["Peter Szolovits and Ozlem Uzuner."],"dc:contributor.department":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:creator":["Tafvizi, Arya (Tafvizi Zavareh)"],"dc:date.accessioned":["2013-02-13T21:23:53Z"],"dc:date.available":["2013-02-13T21:23:53Z"],"dc:date.issued":["2011"],"dc:description":["Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.","This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from PDF version of thesis.","Includes bibliographical references (p. 73-74)."],"dc:description.abstract":["In this thesis, I describe our effort to build an extended and specialized Named Entity Recognizer (NER) to detect instances of Protected Health Information (PHI) in electronic medical records (A de-identifier). The de-identifier was built by creating a comprehensive set of features formed by combining features from the most successful named entity recognizers and de-identifiers and using them in a SVM classifier. We show that the benefit from having an inclusive set of features outweighs the harm from the very large dimensionality of the resulting classification problem. We also show that our classifier does not over-fit the training data. We test whether this approach is more effective than using the NERs separately and combining the results using a committee voting procedure. Finally, we show that our system achieves a precision of up to 1.00, a recall of up to 0.97, and an f-measure of up to 0.98 on a variety of corpora."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/76815"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["A de-identifier for electronic medical records based on a heterogeneous feature set"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:19Z"}