# emrQA: A Large Corpus for Question Answering on Electronic Medical Records

Source: [emrQA: A Large Corpus for Question Answering on Electronic Medical Records](<https://www.sota2.com/research/paper/68fa64c81e209131cea06915>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:11:58.696Z

Authors: Anusri Pampari, Preethi Raghavan, Jennifer Liang, Jian Peng

Published: 2018-09-03T21:56:47.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1809.00732>)

[PDF](<https://arxiv.org/pdf/1809.00732.pdf>)