# DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries arXiv: https://arxiv.org/abs/2205.01290 ยท HTML: https://mlsanity.com/paper/2205.01290/ ## Key facts - Cited by 17 papers in the map (data through 14 Oct 2026). - Builds on 16 of its 30 references that are in the map. - First posted on arXiv 3 May 2022. ## Builds on (most cited first, 16 of 16) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019) - [RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers](https://mlsanity.com/paper/1911.04942/) (2019) - [HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data](https://mlsanity.com/paper/2004.07347/) (2020) - [MultiModalQA: Complex Question Answering over Text, Tables and Images](https://mlsanity.com/paper/2104.06039/) (2021) - [emrQA: A Large Corpus for Question Answering on Electronic Medical Records](https://mlsanity.com/paper/1809.00732/) (2018) - [Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training](https://mlsanity.com/paper/2105.11333/) (2021) - [Text-to-SQL Generation for Question Answering on Electronic Medical Records](https://mlsanity.com/paper/1908.01839/) (2019) - [MMBERT: Multimodal BERT Pretraining for Improved Medical VQA](https://mlsanity.com/paper/2104.01394/) (2021) - [ManyModalQA: Modality Disambiguation and QA over Diverse Inputs](https://mlsanity.com/paper/2001.08034/) (2020) - [A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and Reports](https://mlsanity.com/paper/2009.01523/) (2020) - [CliCR: A Dataset of Clinical Case Reports for Machine Reading Comprehension](https://mlsanity.com/paper/1803.09720/) (2018) - [CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering](https://mlsanity.com/paper/2010.16021/) (2020) - [Entity-Enriched Neural Models for Clinical Question Answering](https://mlsanity.com/paper/2005.06587/) (2020) - [Attention-based Aspect Reasoning for Knowledge Base Question Answering on Clinical Notes](https://mlsanity.com/paper/2108.00513/) (2021) ## Built on by (most cited first, 17 of 17) - [EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images](https://mlsanity.com/paper/2310.18652/) (2023) - [EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records](https://mlsanity.com/paper/2301.07695/) (2023) - [Interpretable LLM-based Table Question Answering](https://mlsanity.com/paper/2412.12386/) (2024) - [Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting](https://mlsanity.com/paper/2305.12723/) (2023) - [Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA](https://mlsanity.com/paper/2503.17933/) (2025) - [SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs](https://mlsanity.com/paper/2402.10979/) (2024) - [Question Answering for Electronic Health Records: A Scoping Review of datasets and models](https://mlsanity.com/paper/2310.08759/) (2023) - [Retrieval Augmented Thought Process for Private Data Handling in Healthcare](https://mlsanity.com/paper/2402.07812/) (2024) - [EHR-SeqSQL : A Sequential Text-to-SQL Dataset For Interactively Exploring Electronic Health Records](https://mlsanity.com/paper/2406.00019/) (2024) - [Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation](https://mlsanity.com/paper/2510.09671/) (2025) - [Evaluating Retrieval-Augmented Generation vs. Long-Context Input for Clinical Reasoning over EHRs](https://mlsanity.com/paper/2508.14817/) (2025) - [Patient-Similarity Cohort Reasoning in Clinical Text-to-SQL](https://mlsanity.com/paper/2601.09876/) (2026) - [RephQA: Evaluating Readability of Large Language Models in Public Health Question Answering](https://mlsanity.com/paper/2509.16360/) (2025) - [CodeClinic: Evaluating Automation of Coding Skills for Clinical Reasoning Agents](https://mlsanity.com/paper/2605.09675/) (2026) - [EHRBench: An Automated and Reliable EHR-based Benchmark for Clinical Decision Making with LLMs](https://mlsanity.com/paper/2605.30637/) (2026) - [EHRNavigator: A Multi-Agent System for Patient-Level Clinical Question Answering over Heterogeneous Electronic Health Records](https://mlsanity.com/paper/2601.10020/) (2026) - [HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering](https://mlsanity.com/paper/2604.21027/) (2026) ## Abstract (arXiv) 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.