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oai:dspace.mit.edu:1721.1/1627392025-09-19T04:49:56Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Ghassemi, Marzyeh Dahleh, Omar Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2025-09-18T14:29:59Z 2025-09-18T14:29:59Z 2025-05 2025-06-23T14:01:43.370Z https://hdl.handle.net/1721.1/162739 This thesis presents a novel approach to the de-identification of clinical notes from Organ Procurement Organization (OPO) records, leveraging advanced natural language processing (NLP) methodologies. Specifically, we employ in-context learning using large language models (LLMs) to effectively identify and remove protected health information (PHI), aiming to maintain high data utility post-redaction. Our work systematically evaluates the performance of the LLM-based method against established baseline techniques, including traditional Named Entity Recognition (NER) and rules-based systems. Through a slew of experiments, we assesses the strengths and limitations of each method regarding precision and recall. This work will contribute to a uniquely extensive dataset, comprising millions of de-identified OPO clinical notes, which will facilitate ethical healthcare research and enhance compliance with contemporary data protection standards. Ultimately, this dataset holds significant potential for improving processes and outcomes within the field of organ donation and procurement. M.Eng. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ Clinical Text De-identification Using Large Language Models: Insights from Organ Procurement Data Thesis application/pdf Master Master of Engineering in Electrical Engineering and Computer Science Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="122e2aa4-3ec0-4029-ac1b-7bb66ed14da8"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Clinical Text De-identification Using Large Language Models: Insights from Organ Procurement Data</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2025-05</PublicationDate> <Authors> <Author> <DisplayName>Dahleh, Omar</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>https://rightsstatements.org/page/InC-EDU/1.0/</License> <Abstract>This thesis presents a novel approach to the de-identification of clinical notes from Organ Procurement Organization (OPO) records, leveraging advanced natural language processing (NLP) methodologies. Specifically, we employ in-context learning using large language models (LLMs) to effectively identify and remove protected health information (PHI), aiming to maintain high data utility post-redaction. Our work systematically evaluates the performance of the LLM-based method against established baseline techniques, including traditional Named Entity Recognition (NER) and rules-based systems. Through a slew of experiments, we assesses the strengths and limitations of each method regarding precision and recall. This work will contribute to a uniquely extensive dataset, comprising millions of de-identified OPO clinical notes, which will facilitate ethical healthcare research and enhance compliance with contemporary data protection standards. Ultimately, this dataset holds significant potential for improving processes and outcomes within the field of organ donation and procurement.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>