DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries

TL;DR

DrugEHRQA is a new dataset with over 70,000 medication-related question-answer pairs from structured tables and unstructured clinical notes, designed to advance multi-modal question answering in electronic health records.

Contribution

The paper introduces the first comprehensive EHR question answering dataset combining structured and unstructured data, and proposes baseline models including a modality selection network and the use of RAT-SQL for complex queries.

Findings

Dataset contains over 70,000 QA pairs.

Baseline model uses modality selection for answer routing.

First application of RAT-SQL to EHR data.

Abstract

This paper develops the first question answering dataset (DrugEHRQA) containing question-answer pairs from both structured tables and unstructured notes from a publicly available Electronic Health Record (EHR). EHRs contain patient records, stored in structured tables and unstructured clinical notes. The information in structured and unstructured EHRs is not strictly disjoint: information may be duplicated, contradictory, or provide additional context between these sources. Our dataset has medication-related queries, containing over 70,000 question-answer pairs. To provide a baseline model and help analyze the dataset, we have used a simple model (MultimodalEHRQA) which uses the predictions of a modality selection network to choose between EHR tables and clinical notes to answer the questions. This is used to direct the questions to the table-based or text-based state-of-the-art QA model. In order to address the problem arising from complex, nested queries, this is the first time Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers (RAT-SQL) has been used to test the structure of query templates in EHR data. Our goal is to provide a benchmark dataset for multi-modal QA systems, and to open up new avenues of research in improving question answering over EHR structured data by using context from unstructured clinical data.