mlsanityData to 14 Oct 2026

Question Answering for Complex Electronic Health Records Database using Unified Encoder-Decoder Architecture

arXiv 2111.14703 ↗First posted 14 Nov 2021cs.CL cs.AI cs.LGcited by 9

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Abstract (arXiv)

An intelligent machine that can answer human questions based on electronic health records (EHR-QA) has a great practical value, such as supporting clinical decisions, managing hospital administration, and medical chatbots. Previous table-based QA studies focusing on translating natural questions into table queries (NLQ2SQL), however, suffer from the unique nature of EHR data due to complex and specialized medical terminology, hence increased decoding difficulty. In this paper, we design UniQA, a unified encoder-decoder architecture for EHR-QA where natural language questions are converted to queries such as SQL or SPARQL. We also propose input masking (IM), a simple and effective method to cope with complex medical terms and various typos and better learn the SQL/SPARQL syntax. Combining the unified architecture with an effective auxiliary training objective, UniQA demonstrated a significant performance improvement against the previous state-of-the-art model for MIMICSQL* (14.2% gain), the most complex NLQ2SQL dataset in the EHR domain, and its typo-ridden versions (approximately 28.8% gain). In addition, we confirmed consistent results for the graph-based EHR-QA dataset, MIMICSPARQL*.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 17 of its 32 references that are in the map. First posted on arXiv 14 Nov 2021.

Builds on

17 of 32 references.

References · 17

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5 more
  1. 2018Coarse-to-Fine Decoding for Neural Semantic Parsingcited by 110
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 0; 2025: 0; 2026: 4.

9 papers.

  1. 2023EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Imagescited by 53
  2. 2023EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Recordscited by 45
  3. 2023LeafAI: query generator for clinical cohort discovery rivaling a human programmercited by 9
  4. 2023Question Answering for Electronic Health Records: A Scoping Review of datasets and modelscited by 7
  5. 2022Uncertainty-Aware Text-to-Program for Question Answering on Structured Electronic Health Recordscited by 4
  6. 2026EHRNavigator: A Multi-Agent System for Patient-Level Clinical Question Answering over Heterogeneous Electronic Health Recordscited by 1
  7. 2026CBR-to-SQL: Rethinking Retrieval-based Text-to-SQL using Case-based Reasoning in the Healthcare Domaincited by 0
  8. 2026M3 QuestionIng: Multi-modal Multi-span Medical Question Answeringcited by 0
  9. 2026Large Language Models for Structured Clinical Data Analysis: Dual-Agent Grounding and Validationcited by 0