dc.contributor.advisor | Sontag, David | |
dc.contributor.author | Agrawal, Monica | |
dc.date.accessioned | 2023-03-31T14:28:16Z | |
dc.date.available | 2023-03-31T14:28:16Z | |
dc.date.issued | 2023-02 | |
dc.date.submitted | 2023-02-28T14:39:21.578Z | |
dc.identifier.uri | https://hdl.handle.net/1721.1/150049 | |
dc.description.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. | |
dc.publisher | Massachusetts Institute of Technology | |
dc.rights | In Copyright - Educational Use Permitted | |
dc.rights | 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 | |
dc.description.degree | Ph.D. | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
mit.thesis.degree | Doctoral | |
thesis.degree.name | Doctor of Philosophy | |