Medical abstract inference dataset

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.