Annotating Electronic Medical Records for Question Answering

Understand

Our research is in the relatively unexplored area of question answering technologies for patient-specific questions over their electronic health records.

• A large dataset of human expert curated question and answer pairs is an important pre-requisite for developing, training and evaluating any question answering system that is powered by machine learning.
• In this paper, we describe a process for creating such a dataset of questions and answers.
• Our methodology is replicable, can be conducted by medical students as annotators, and results in high inter-annotator agreement (0.71 Cohen's kappa).