3 citations · 7 across the 9 of their papers we have counts for
9 papers
Multi-lingual Multi-institutional Electronic Health Record based Predictive Model
Kyunghoon Hur, Heeyoung Kwak, Jinsu Jang +2
Large-scale EHR prediction across institutions is hindered by substantial heterogeneity in schemas and code systems. Although Common Data Models (CDMs) can standardize records for…
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…
From Conversation to Query Execution: Benchmarking User and Tool Interactions for EHR Database Agents
Gyubok Lee, Woosog Chay, Heeyoung Kwak +5
Despite the impressive performance of LLM-powered agents, their adoption for Electronic Health Record (EHR) data access remains limited by the absence of benchmarks that adequately…
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…
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…
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…
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…
TrustSQL: Benchmarking Text-to-SQL Reliability with Penalty-Based Scoring
Gyubok Lee, Woosog Chay, Seonhee Cho +1
Text-to-SQL enables users to interact with databases using natural language, simplifying the retrieval and synthesis of information. Despite the remarkable success of large languag…
KorMedMCQA: Multi-Choice Question Answering Benchmark for Korean Healthcare Professional Licensing Examinations
Sunjun Kweon, Byungjin Choi, Gyouk Chu +7
We present KorMedMCQA, the first Korean Medical Multiple-Choice Question Answering benchmark, derived from professional healthcare licensing examinations conducted in Korea between…