Towards Scalable Structured Data from Clinical Text
Name
Agrawal-magrawal-PhD-EECS-2023-thesis.pdf
Description
Thesis PDF
Size
4.56 MB
Format
Adobe PDF
Checksum (MD5)
e57ced8e6a0b2321a3f7d4b9b3c96687
Author(s)
Agrawal, Monica
Advisor(s)
Sontag, David
Date Issued
February 2023
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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In Copyright - Educational Use Permitted
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