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

arXiv: https://arxiv.org/abs/2111.14703 · HTML: https://mlsanity.com/paper/2111.14703/

## 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 (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://mlsanity.com/paper/1709.00103/) (2017)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers](https://mlsanity.com/paper/1911.04942/) (2019)
- [Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation](https://mlsanity.com/paper/1905.08205/) (2019)
- [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://mlsanity.com/paper/1907.12461/) (2019)
- [emrQA: A Large Corpus for Question Answering on Electronic Medical Records](https://mlsanity.com/paper/1809.00732/) (2018)
- [Coarse-to-Fine Decoding for Neural Semantic Parsing](https://mlsanity.com/paper/1805.04793/) (2018)
- [A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization](https://mlsanity.com/paper/1902.01069/) (2019)
- [Text-to-SQL Generation for Question Answering on Electronic Medical Records](https://mlsanity.com/paper/1908.01839/) (2019)
- [CliCR: A Dataset of Clinical Case Reports for Machine Reading Comprehension](https://mlsanity.com/paper/1803.09720/) (2018)
- [Knowledge Graph-based Question Answering with Electronic Health Records](https://mlsanity.com/paper/2010.09394/) (2020)

## Built on by (most cited first, 9 of 9)

- [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)
- [LeafAI: query generator for clinical cohort discovery rivaling a human programmer](https://mlsanity.com/paper/2304.06203/) (2023)
- [Question Answering for Electronic Health Records: A Scoping Review of datasets and models](https://mlsanity.com/paper/2310.08759/) (2023)
- [Uncertainty-Aware Text-to-Program for Question Answering on Structured Electronic Health Records](https://mlsanity.com/paper/2203.06918/) (2022)
- [EHRNavigator: A Multi-Agent System for Patient-Level Clinical Question Answering over Heterogeneous Electronic Health Records](https://mlsanity.com/paper/2601.10020/) (2026)
- [CBR-to-SQL: Rethinking Retrieval-based Text-to-SQL using Case-based Reasoning in the Healthcare Domain](https://mlsanity.com/paper/2603.05569/) (2026)
- [M3 QuestionIng: Multi-modal Multi-span Medical Question Answering](https://mlsanity.com/paper/2606.28329/) (2026)
- [Large Language Models for Structured Clinical Data Analysis: Dual-Agent Grounding and Validation](https://mlsanity.com/paper/2609.34039/) (2026)

## 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*.