mlsanityData to 14 Oct 2026

Text-to-SQL Generation for Question Answering on Electronic Medical Records

arXiv 1908.01839 ↗First posted 28 Jul 2019cs.CL cs.AI cs.IRcited by 77

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Abstract (arXiv)

Electronic medical records (EMR) contain comprehensive patient information and are typically stored in a relational database with multiple tables. Effective and efficient patient information retrieval from EMR data is a challenging task for medical experts. Question-to-SQL generation methods tackle this problem by first predicting the SQL query for a given question about a database, and then, executing the query on the database. However, most of the existing approaches have not been adapted to the healthcare domain due to a lack of healthcare Question-to-SQL dataset for learning models specific to this domain. In addition, wide use of the abbreviation of terminologies and possible typos in questions introduce additional challenges for accurately generating the corresponding SQL queries. In this paper, we tackle these challenges by developing a deep learning based TRanslate-Edit Model for Question-to-SQL (TREQS) generation, which adapts the widely used sequence-to-sequence model to directly generate the SQL query for a given question, and further performs the required edits using an attentive-copying mechanism and task-specific look-up tables. Based on the widely used publicly available electronic medical database, we create a new large-scale Question-SQL pair dataset, named MIMICSQL, in order to perform the Question-to-SQL generation task in healthcare domain. An extensive set of experiments are conducted to evaluate the performance of our proposed model on MIMICSQL. Both quantitative and qualitative experimental results indicate the flexibility and efficiency of our proposed method in predicting condition values and its robustness to random questions with abbreviations and typos.

Key facts. Cited by 77 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Jul 2019.

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Citing papers per year: 2021: 7; 2022: 9; 2023: 15; 2024: 15; 2025: 17; 2026: 14.

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  1. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 688
  2. 2024Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflowscited by 232
  3. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
  4. 2024EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Recordscited by 78
  5. 2024A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?cited by 56
  6. 2023ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogramcited by 54
  7. 2023EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Imagescited by 53
  8. 2022Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expectcited by 47
  9. 2021Biomedical Question Answering: A Survey of Approaches and Challengescited by 46
  10. 2025DABstep: Data Agent Benchmark for Multi-step Reasoningcited by 46
  11. 2023EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Recordscited by 45
  12. 2023ScienceBenchmark: A Complex Real-World Benchmark for Evaluating Natural Language to SQL Systemscited by 45
38 more
  1. 2021OCT-GAN: Neural ODE-based Conditional Tabular GANscited by 27
  2. 2023Natural Language Interfaces for Tabular Data Querying and Visualization: A Surveycited by 27
  3. 2025MedAgentGym: A Scalable Agentic Training Environment for Code-Centric Reasoning in Biomedical Data Sciencecited by 23
  4. 2025FHIR-AgentBench: Benchmarking LLM Agents for Realistic Interoperable EHR Question Answeringcited by 22
  5. 2022DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queriescited by 17
  6. 2021Natural Language Processing for Smart Healthcarecited by 14
  7. 2023Semantic Decomposition of Question and SQL for Text-to-SQL Parsingcited by 14
  8. 2024SM3-Text-to-Query: Synthetic Multi-Model Medical Text-to-Query Benchmarkcited by 14
  9. 2023A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promisescited by 13
  10. 2024Evaluating the Data Model Robustness of Text-to-SQL Systems Based on Real User Queriescited by 12
  11. 2025ExeSQL: Self-Taught Text-to-SQL Models with Execution-Driven Bootstrapping for SQL Dialectscited by 12
  12. 2024Overview of the EHRSQL 2024 Shared Task on Reliable Text-to-SQL Modeling on Electronic Health Recordscited by 11
  13. 2021A Scoping Review of Publicly Available Language Tasks in Clinical Natural Language Processingcited by 10
  14. 2023Testing the Limits of Unified Sequence to Sequence LLM Pretraining on Diverse Table Data Taskscited by 10
  15. 2024BookSQL: A Large Scale Text-to-SQL Dataset for Accounting Domaincited by 10
  16. 2021Question Answering for Complex Electronic Health Records Database using Unified Encoder-Decoder Architecturecited by 9
  17. 2023Towards Understanding the Generalization of Medical Text-to-SQL Models and Datasetscited by 9
  18. 2023LeafAI: query generator for clinical cohort discovery rivaling a human programmercited by 9
  19. 2023Reboost Large Language Model-based Text-to-SQL, Text-to-Python, and Text-to-Function -- with Real Applications in Traffic Domaincited by 9
  20. 2024Almanac Copilot: Towards Autonomous Electronic Health Record Navigationcited by 9
  21. 2025Sparks of Tabular Reasoning via Text2SQL Reinforcement Learningcited by 9
  22. 2024Retrieval augmented text-to-SQL generation for epidemiological question answering using electronic health recordscited by 8
  23. 2022Learn from Yesterday: A Semi-Supervised Continual Learning Method for Supervision-Limited Text-to-SQL Task Streamscited by 7
  24. 2023Question Answering for Electronic Health Records: A Scoping Review of datasets and modelscited by 7
  25. 2024LG AI Research & KAIST at EHRSQL 2024: Self-Training Large Language Models with Pseudo-Labeled Unanswerable Questions for a Reliable Text-to-SQL System on EHRscited by 7
  26. 2022Artificial Intelligence for In Silico Clinical Trials: A Reviewcited by 6
  27. 2024Unmasking Database Vulnerabilities: Zero-Knowledge Schema Inference Attacks in Text-to-SQL Systemscited by 6
  28. 2023Parameterizing Context: Unleashing the Power of Parameter-Efficient Fine-Tuning and In-Context Tuning for Continual Table Semantic Parsingcited by 5
  29. 2024EHR-SeqSQL : A Sequential Text-to-SQL Dataset For Interactively Exploring Electronic Health Recordscited by 5
  30. 2021Attention-based Aspect Reasoning for Knowledge Base Question Answering on Clinical Notescited by 4
  31. 2022Uncertainty-Aware Text-to-Program for Question Answering on Structured Electronic Health Recordscited by 4
  32. 2025Generating patient cohorts from electronic health records using two-step retrieval-augmented text-to-SQL generationcited by 4
  33. 2025BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge Basescited by 4
  34. 2025Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunitiescited by 4
  35. 2026AgentEHR: Advancing Autonomous Clinical Decision-Making via Retrospective Summarizationcited by 4
  36. 2026ClinEnv: An Interactive Multi-Stage Long Horizon EHR Environment for Agentscited by 4
  37. 2022On the Security Vulnerabilities of Text-to-SQL Modelscited by 3
  38. 2024Towards Unbiased Evaluation of Detecting Unanswerable Questions in EHRSQLcited by 3

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