# Knowledge Graph-based Question Answering with Electronic Health Records

arXiv: https://arxiv.org/abs/2010.09394 · HTML: https://mlsanity.com/paper/2010.09394/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 19 of 19)

- [Biomedical Question Answering: A Survey of Approaches and Challenges](https://mlsanity.com/paper/2102.05281/) (2021)
- [EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records](https://mlsanity.com/paper/2301.07695/) (2023)
- [SM3-Text-to-Query: Synthetic Multi-Model Medical Text-to-Query Benchmark](https://mlsanity.com/paper/2411.05521/) (2024)
- [Graph-Text Multi-Modal Pre-training for Medical Representation Learning](https://mlsanity.com/paper/2203.09994/) (2022)
- [Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges](https://mlsanity.com/paper/2508.00217/) (2025)
- [Question Answering for Complex Electronic Health Records Database using Unified Encoder-Decoder Architecture](https://mlsanity.com/paper/2111.14703/) (2021)
- [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)
- [LG AI Research & KAIST at EHRSQL 2024: Self-Training Large Language Models with Pseudo-Labeled Unanswerable Questions for a Reliable Text-to-SQL System on EHRs](https://mlsanity.com/paper/2405.11162/) (2024)
- [Uncertainty-Aware Text-to-Program for Question Answering on Structured Electronic Health Records](https://mlsanity.com/paper/2203.06918/) (2022)
- [BioMol-MQA: A Multi-Modal Question Answering Dataset For LLM Reasoning Over Bio-Molecular Interactions](https://mlsanity.com/paper/2506.05766/) (2025)
- [FathomGPT: A Natural Language Interface for Interactively Exploring Ocean Science Data](https://mlsanity.com/paper/2412.02784/) (2024)
- [A Comparative Study of Question Answering over Knowledge Bases](https://mlsanity.com/paper/2211.08170/) (2022)
- [EHRBench: An Automated and Reliable EHR-based Benchmark for Clinical Decision Making with LLMs](https://mlsanity.com/paper/2605.30637/) (2026)
- [From EMR Data to Clinical Insight: An LLM-Driven Framework for Automated Pre-Consultation Questionnaire Generation](https://mlsanity.com/paper/2508.00581/) (2025)
- [HealthBranches: Synthesizing Clinically-Grounded Question Answering Datasets via Decision Pathways](https://mlsanity.com/paper/2508.07308/) (2025)
- [EHRNavigator: A Multi-Agent System for Patient-Level Clinical Question Answering over Heterogeneous Electronic Health Records](https://mlsanity.com/paper/2601.10020/) (2026)
- [CLIFT: Analysing Natural Distribution Shift on Question Answering Models in Clinical Domain](https://mlsanity.com/paper/2310.13146/) (2023)
- [SCARE: A Benchmark for SQL Correction and Question Answerability Classification for Reliable EHR Question Answering](https://mlsanity.com/paper/2511.17559/) (2025)

## Abstract (arXiv)

Question Answering (QA) is a widely-used framework for developing and evaluating an intelligent machine. In this light, QA on Electronic Health Records (EHR), namely EHR QA, can work as a crucial milestone towards developing an intelligent agent in healthcare. EHR data are typically stored in a relational database, which can also be converted to a directed acyclic graph, allowing two approaches for EHR QA: Table-based QA and Knowledge Graph-based QA. We hypothesize that the graph-based approach is more suitable for EHR QA as graphs can represent relations between entities and values more naturally compared to tables, which essentially require JOIN operations. In this paper, we propose a graph-based EHR QA where natural language queries are converted to SPARQL instead of SQL. To validate our hypothesis, we create four EHR QA datasets (graph-based VS table-based, and simplified database schema VS original database schema), based on a table-based dataset MIMICSQL. We test both a simple Seq2Seq model and a state-of-the-art EHR QA model on all datasets where the graph-based datasets facilitated up to 34% higher accuracy than the table-based dataset without any modification to the model architectures. Finally, all datasets are open-sourced to encourage further EHR QA research in both directions.