Towards Scalable Structured Data from Clinical Text

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.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Agrawal, Monica

Advisor dc:contributor.advisor

• Sontag, David

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/150049

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/150049