Medical abstract inference dataset
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2018
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| Online Access: | http://hdl.handle.net/1721.1/119516 |
| _version_ | 1811092700923953152 |
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| author | De León, Eduardo Enrique |
| author2 | Regina Barzilay. |
| author_facet | Regina Barzilay. De León, Eduardo Enrique |
| author_sort | De León, Eduardo Enrique |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. |
| first_indexed | 2024-09-23T15:22:35Z |
| format | Thesis |
| id | mit-1721.1/119516 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T15:22:35Z |
| publishDate | 2018 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1195162019-04-10T13:13:46Z Medical abstract inference dataset De León, Eduardo Enrique Regina Barzilay. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. 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). 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. by Eduardo Enrique de León. M. Eng. 2018-12-11T20:38:23Z 2018-12-11T20:38:23Z 2017 2017 Thesis http://hdl.handle.net/1721.1/119516 1066344951 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 35 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. De León, Eduardo Enrique Medical abstract inference dataset |
| title | Medical abstract inference dataset |
| title_full | Medical abstract inference dataset |
| title_fullStr | Medical abstract inference dataset |
| title_full_unstemmed | Medical abstract inference dataset |
| title_short | Medical abstract inference dataset |
| title_sort | medical abstract inference dataset |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/119516 |
| work_keys_str_mv | AT deleoneduardoenrique medicalabstractinferencedataset |