Given a treatment A, a comparator B, and an outcome, infer the reported relationship between A and B with respect to outcome, and provide evidence supporting this from the text.
Rather than turn to large crowdsourcing platforms, like Amazon Mechanical Turk, we decided to hire a group of doctors via Upwork, as we felt that the quality of annotations would be of a higher standard. We split the group of doctors into three groups: Prompt Generation, Annotation, and Verification.
The immense growth of published randomized control trials impedes a doctor’s ability to confidently determine what interventions are most suited to a given problem, as it is not feasible for medical professionals to parse through enormous amounts existing literature. The goal of our research project is to generate avante-garde models and a new corpora, in order to automatically extract results for pairs of interventions, comparators, and outcomes with respect to a specific paper describing a research trial.
This work is supported by NSF CAREER Award 1750978.
* Special thank you for the Newsies website for layout inspiration (https://summari.es/).