Search Results for author: Seongsu Bae

Found 10 papers, 8 papers with code

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

1 code implementation24 Jun 2024 Yeonsu Kwon, Jiho Kim, Gyubok Lee, Seongsu Bae, Daeun Kyung, Wonchul Cha, Tom Pollard, Alistair Johnson, Edward Choi

To address this, we developed EHRCon, a new dataset and task specifically designed to ensure data consistency between structured tables and unstructured notes in EHRs.

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

1 code implementation4 May 2024 Gyubok Lee, Sunjun Kweon, Seongsu Bae, Edward Choi

In this paper, we describe the task of reliable text-to-SQL modeling, the dataset, and the methods and results of the participants.

Information Retrieval Question Answering +1

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

3 code implementations NeurIPS 2023 Seongsu Bae, Daeun Kyung, Jaehee Ryu, Eunbyeol Cho, Gyubok Lee, Sunjun Kweon, JungWoo Oh, Lei Ji, Eric I-Chao Chang, Tackeun Kim, Edward Choi

To develop our dataset, we first construct two uni-modal resources: 1) The MIMIC-CXR-VQA dataset, our newly created medical visual question answering (VQA) benchmark, specifically designed to augment the imaging modality in EHR QA, and 2) EHRSQL (MIMIC-IV), a refashioned version of a previously established table-based EHR QA dataset.

Decision Making Medical Visual Question Answering +2

Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes

1 code implementation1 Sep 2023 Sunjun Kweon, Junu Kim, Jiyoun Kim, Sujeong Im, Eunbyeol Cho, Seongsu Bae, JungWoo Oh, Gyubok Lee, Jong Hak Moon, Seng Chan You, Seungjin Baek, Chang Hoon Han, Yoon Bin Jung, Yohan Jo, Edward Choi

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 privacy regulations.

Language Modelling Large Language Model

ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogram

1 code implementation NeurIPS 2023 JungWoo Oh, Gyubok Lee, Seongsu Bae, Joon-Myoung Kwon, Edward Choi

As a result, our dataset includes diverse ECG interpretation questions, including those that require a comparative analysis of two different ECGs.

Question Answering

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

1 code implementation NeurIPS 2022 Datasets and Benchmarks 2022 Gyubok Lee, Hyeonji Hwang, Seongsu Bae, Yeonsu Kwon, Woncheol Shin, Seongjun Yang, Minjoon Seo, Jong-Yeup Kim, Edward Choi

We then manually linked these questions to two open-source EHR databases, MIMIC-III and eICU, and included various time expressions and held-out unanswerable questions in the dataset, which were also collected from the poll.

Retrieval Text-To-SQL

Graph-Text Multi-Modal Pre-training for Medical Representation Learning

1 code implementation18 Mar 2022 Sungjin Park, Seongsu Bae, Jiho Kim, Tackeun Kim, Edward Choi

MedGTX uses a novel graph encoder to exploit the graphical nature of structured EHR data, and a text encoder to handle unstructured text, and a cross-modal encoder to learn a joint representation space.

Representation Learning

Uncertainty-Aware Text-to-Program for Question Answering on Structured Electronic Health Records

1 code implementation14 Mar 2022 Daeyoung Kim, Seongsu Bae, Seungho Kim, Edward Choi

In addition, for a reliable EHR-QA model, we apply the uncertainty decomposition method to measure the ambiguity in the input question.

Natural Language Queries Question Answering

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

no code implementations14 Nov 2021 Seongsu Bae, Daeyoung Kim, Jiho Kim, Edward Choi

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.

Decoder Natural Questions +1

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