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

arXiv 1809.00732 ↗First posted 3 Sep 2018cs.CLcited by 134

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Abstract (arXiv)

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.

Key facts. Cited by 134 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Sep 2018.

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Citing papers per year: 2021: 17; 2022: 20; 2023: 30; 2024: 24; 2025: 20; 2026: 23.

134 papers.

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  1. 2022Large Language Models Encode Clinical Knowledgecited by 1,807
  2. 2021A Dataset of Information-Seeking Questions and Answers Anchored in Research Paperscited by 475
  3. 2022GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Recordscited by 238
  4. 2021When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Datasetcited by 199
  5. 2022Cross-modal Memory Networks for Radiology Report Generationcited by 183
  6. 2023A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethicscited by 162
  7. 2023A Study of Generative Large Language Model for Medical Research and Healthcarecited by 160
  8. 2024Benchmark Data Contamination of Large Language Models: A Surveycited by 138
  9. 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challengecited by 123
  10. 2023CMB: A Comprehensive Medical Benchmark in Chinesecited by 113
  11. 2024Benchmarking Large Language Models on Answering and Explaining Challenging Medical Questionscited by 106
  12. 2023Do We Still Need Clinical Language Models?cited by 97
38 more
  1. 2023ExpertQA: Expert-Curated Questions and Attributed Answerscited by 96
  2. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
  3. 2023Benchmarking Large Language Models on CMExam -- A Comprehensive Chinese Medical Exam Datasetcited by 90
  4. 2021Pre-trained Language Models in Biomedical Domain: A Systematic Surveycited by 87
  5. 2023Towards Mitigating Hallucination in Large Language Models via Self-Reflectioncited by 81
  6. 2024SPIQA: A Dataset for Multimodal Question Answering on Scientific Paperscited by 75
  7. 2021Memorization vs. Generalization: Quantifying Data Leakage in NLP Performance Evaluationcited by 69
  8. 2022Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequencescited by 67
  9. 2023MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Recordscited by 54
  10. 2023EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Imagescited by 53
  11. 2023ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogramcited by 52
  12. 2021Biomedical Question Answering: A Survey of Approaches and Challengescited by 46
  13. 2023EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Recordscited by 42
  14. 2025MedicalAgentsBench for Complex Medical Reasoning: Comparing Internalized Reasoning Models versus Externalized Agent-based Frameworkscited by 38
  15. 2023A Comparative Study of Pretrained Language Models for Long Clinical Textcited by 35
  16. 2024A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directionscited by 34
  17. 2025A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontierscited by 34
  18. 2024EHRNoteQA: An LLM Benchmark for Real-World Clinical Practice Using Discharge Summariescited by 33
  19. 2021AMMU : A Survey of Transformer-based Biomedical Pretrained Language Modelscited by 32
  20. 2024HealthQ: Unveiling Questioning Capabilities of LLM Chains in Healthcare Conversationscited by 31
  21. 2021CodeQA: A Question Answering Dataset for Source Code Comprehensioncited by 30
  22. 2021English Machine Reading Comprehension Datasets: A Surveycited by 26
  23. 2022KALA: Knowledge-Augmented Language Model Adaptationcited by 21
  24. 2023PaniniQA: Enhancing Patient Education Through Interactive Question Answeringcited by 21
  25. 2023MedEval: A Multi-Level, Multi-Task, and Multi-Domain Medical Benchmark for Language Model Evaluationcited by 19
  26. 2024MedSyn: LLM-based Synthetic Medical Text Generation Frameworkcited by 19
  27. 2021CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notescited by 18
  28. 2022DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queriescited by 17
  29. 2024Unveiling LLM Evaluation Focused on Metrics: Challenges and Solutionscited by 17
  30. 2021Q-Pain: A Question Answering Dataset to Measure Social Bias in Pain Managementcited by 16
  31. 2022Benchmark datasets driving artificial intelligence development fail to capture the needs of medical professionalscited by 16
  32. 2022Learning to Ask Like a Physiciancited by 16
  33. 2024Large Language Model Benchmarks in Medical Taskscited by 16
  34. 2025PaperArena: An Evaluation Benchmark for Tool-Augmented Agentic Reasoning on Scientific Literaturecited by 15
  35. 2022DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processingcited by 14
  36. 2023UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?cited by 14
  37. 2022Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understandingcited by 13
  38. 2024PoTable: Towards Systematic Thinking via Plan-then-Execute Stage Reasoning on Tablescited by 13

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