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20202026
most citedEHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records

13 citations · 32 across the 19 of their papers we have counts for

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10 papers · 1 filter

cs.CL2025

SCARE: A Benchmark for SQL Correction and Question Answerability Classification for Reliable EHR Question Answering

Gyubok Lee, Woosog Chay, Edward Choi

Recent advances in Large Language Models (LLMs) have enabled the development of text-to-SQL models that allow clinicians to query structured data stored in Electronic Health Record…

cs.CL2025★ 2 cited

FHIR-AgentBench: Benchmarking LLM Agents for Realistic Interoperable EHR Question Answering

Gyubok Lee, Elea Bach, Eric Yang +5

The recent shift toward the Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR) standard opens a new frontier for clinical AI, demanding LLM agents to navigate…

cs.CL2024

EHR-SeqSQL : A Sequential Text-to-SQL Dataset For Interactively Exploring Electronic Health Records

Jaehee Ryu, Seonhee Cho, Gyubok Lee +1

In this paper, we introduce EHR-SeqSQL, a novel sequential text-to-SQL dataset for Electronic Health Record (EHR) databases. EHR-SeqSQL is designed to address critical yet underexp…

cs.CL2024

EHRCon: Dataset for Checking Consistency between Unstructured Notes and Structured Tables in Electronic Health Records

Yeonsu Kwon, Jiho Kim, Gyubok Lee +6

Electronic Health Records (EHRs) are integral for storing comprehensive patient medical records, combining structured data (e.g., medications) with detailed clinical notes (e.g., p…

cs.CL2024★ 1 cited

Overview of the EHRSQL 2024 Shared Task on Reliable Text-to-SQL Modeling on Electronic Health Records

Gyubok Lee, Sunjun Kweon, Seongsu Bae +1

Electronic Health Records (EHRs) are relational databases that store the entire medical histories of patients within hospitals. They record numerous aspects of patients' medical ca…

cs.CL2024

Towards Unbiased Evaluation of Detecting Unanswerable Questions in EHRSQL

Yongjin Yang, Sihyeon Kim, SangMook Kim +3

Incorporating unanswerable questions into EHR QA systems is crucial for testing the trustworthiness of a system, as providing non-existent responses can mislead doctors in their di…

cs.CL2023★ 7 cited

EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images

Seongsu Bae, Daeun Kyung, Jaehee Ryu +8

Electronic Health Records (EHRs), which contain patients' medical histories in various multi-modal formats, often overlook the potential for joint reasoning across imaging and tabl…

cs.CL2023★ 5 cited

Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes

Sunjun Kweon, Junu Kim, Jiyoun Kim +12

The development of large language models tailored for handling patients' clinical notes is often hindered by the limited accessibility and usability of these notes due to strict pr…

cs.CL2023★ 13 cited

EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records

Gyubok Lee, Hyeonji Hwang, Seongsu Bae +6

We present a new text-to-SQL dataset for electronic health records (EHRs). The utterances were collected from 222 hospital staff members, including physicians, nurses, and insuranc…

cs.CL2021

Improving Lexically Constrained Neural Machine Translation with Source-Conditioned Masked Span Prediction

Gyubok Lee, Seongjun Yang, Edward Choi

Accurate terminology translation is crucial for ensuring the practicality and reliability of neural machine translation (NMT) systems. To address this, lexically constrained NMT ex…