emrQA: A Large Corpus for Question Answering on Electronic Medical Records
This provides a domain-specific resource for QA in healthcare, addressing a bottleneck for researchers working with clinical notes, though it is incremental as it re-purposes existing annotations.
The authors tackled the lack of large-scale question answering datasets for electronic medical records by generating emrQA, a corpus with 1 million question-logical form and 400,000+ question-answer evidence pairs, using existing expert annotations from i2b2 datasets.
We propose a novel methodology to generate domain-specific large-scale question answering (QA) datasets by re-purposing existing annotations for other NLP tasks. We demonstrate an instance of this methodology in generating a large-scale QA dataset for electronic medical records by leveraging existing expert annotations on clinical notes for various NLP tasks from the community shared i2b2 datasets. The resulting corpus (emrQA) has 1 million question-logical form and 400,000+ question-answer evidence pairs. We characterize the dataset and explore its learning potential by training baseline models for question to logical form and question to answer mapping.