13 citations · 32 across the 12 of their papers we have counts for
12 papers
KorMedMCQA-V: A Multimodal Benchmark for Evaluating Vision-Language Models on the Korean Medical Licensing Examination
Byungjin Choi, Seongsu Bae, Sunjun Kweon +1
We introduce KorMedMCQA-V, a Korean medical licensing-exam-style multimodal multiple-choice question answering benchmark for evaluating vision-language models (VLMs). The dataset c…
PatientSim: A Persona-Driven Simulator for Realistic Doctor-Patient Interactions
Daeun Kyung, Hyunseung Chung, Seongsu Bae +5
Doctor-patient consultations require multi-turn, context-aware communication tailored to diverse patient personas. Training or evaluating doctor LLMs in such settings requires real…
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…
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…
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…
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…
KU-DMIS-MSRA at RadSum23: Pre-trained Vision-Language Model for Radiology Report Summarization
Gangwoo Kim, Hajung Kim, Lei Ji +7
In this paper, we introduce CheXOFA, a new pre-trained vision-language model (VLM) for the chest X-ray domain. Our model is initially pre-trained on various multimodal datasets wit…
ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogram
Jungwoo Oh, Gyubok Lee, Seongsu Bae +2
Question answering (QA) in the field of healthcare has received much attention due to significant advancements in natural language processing. However, existing healthcare QA datas…
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…
Uncertainty-Aware Text-to-Program for Question Answering on Structured Electronic Health Records
Daeyoung Kim, Seongsu Bae, Seungho Kim +1
Question Answering on Electronic Health Records (EHR-QA) has a significant impact on the healthcare domain, and it is being actively studied. Previous research on structured EHR-QA…
Graph-Text Multi-Modal Pre-training for Medical Representation Learning
Sungjin Park, Seongsu Bae, Jiho Kim +2
As the volume of Electronic Health Records (EHR) sharply grows, there has been emerging interest in learning the representation of EHR for healthcare applications. Representation l…
Question Answering for Complex Electronic Health Records Database using Unified Encoder-Decoder Architecture
Seongsu Bae, Daeyoung Kim, Jiho Kim +1
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 hosp…