{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/150049"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/150049","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Towards Scalable Structured Data from Clinical Text","abstract":"The adoption of electronic health records (EHRs) presents an incredible opportunity to improve medicine both at the point-of-care and through retrospective research. Unfortunately, many pertinent variables are trapped in unstructured clinical note text. Automated extraction is difficult since clinical notes are written in their own jargon-heavy dialect, patient histories can contain hundreds of notes, and there is often minimal labeled data. In this thesis, I tackle these barriers from three interconnected angles: (i) the design of human-AI teams to speed up annotation workflows, (ii) the development of label-efficient modeling methods, and (iii) a re-design of electronic health records that incentivizes cleaner data at time of creation.","abstract_html":"The adoption of electronic health records (EHRs) presents an incredible opportunity to improve medicine both at the point-of-care and through retrospective research. Unfortunately, many pertinent variables are trapped in unstructured clinical note text. Automated extraction is difficult since clinical notes are written in their own jargon-heavy dialect, patient histories can contain hundreds of notes, and there is often minimal labeled data. In this thesis, I tackle these barriers from three interconnected angles: (i) the design of human-AI teams to speed up annotation workflows, (ii) the development of label-efficient modeling methods, and (iii) a re-design of electronic health records that incentivizes cleaner data at time of creation.","abstract_has_math":false,"creators":["Agrawal, Monica"],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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Unfortunately, many pertinent variables are trapped in unstructured clinical note text. Automated extraction is difficult since clinical notes are written in their own jargon-heavy dialect, patient histories can contain hundreds of notes, and there is often minimal labeled data. In this thesis, I tackle these barriers from three interconnected angles: (i) the design of human-AI teams to speed up annotation workflows, (ii) the development of label-efficient modeling methods, and (iii) a re-design of electronic health records that incentivizes cleaner data at time of creation."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Towards Scalable Structured Data from Clinical Text"]}]}],"canonical_facts":{"dc:contributor.advisor":["Sontag, David"],"dc:contributor.department":["Massachusetts Institute of Technology. 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In this thesis, I tackle these barriers from three interconnected angles: (i) the design of human-AI teams to speed up annotation workflows, (ii) the development of label-efficient modeling methods, and (iii) a re-design of electronic health records that incentivizes cleaner data at time of creation."],"dc:description.degree":["Ph.D."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/150049"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Towards Scalable Structured Data from Clinical Text"],"dc:type":["Thesis"],"thesis:degree_name":["Doctoral","Doctor of Philosophy"]},"updated_at":"2026-07-22T22:21:05Z"}