Medical abstract inference dataset
Name
1066344951-MIT.pdf
Description
Full printable version
Size
657.5 KB
Format
Adobe PDF
Checksum (MD5)
6729fa149b9d2797b973bdd65304c793
Author(s)
De León, Eduardo Enrique
Advisor(s)
Regina Barzilay.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, I built a dataset for predicting clinical outcomes from medical abstracts and their title. Medical Abstract Inference consists of 1,794 data points. Titles were filtered to include the abstract's reported medical intervention and clinical outcome. Data points were annotated with the interventions effect on the outcome. Resulting labels were one of the following: increased, decreased, or had no significant difference on the outcome. In addition, rationale sentences were marked, these sentences supply the necessary supporting evidence for the overall prediction. Preliminary modeling was also done to evaluate the corpus. Preliminary models included top performing Natural Language Inference models as well as Rationale based models and linear classifiers.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (page 35).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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