Search Results for author: Edward Choi

Found 73 papers, 47 papers with code

Towards Unbiased Evaluation of Detecting Unanswerable Questions in EHRSQL

no code implementations29 Apr 2024 Yongjin Yang, Sihyeon Kim, Sangmook Kim, Gyubok Lee, Se-Young Yun, Edward Choi

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 diagnoses.

EHRFL: Federated Learning Framework for Heterogeneous EHRs and Precision-guided Selection of Participating Clients

1 code implementation20 Apr 2024 Jiyoun Kim, Junu Kim, Kyunghoon Hur, Edward Choi

In this study, we provide solutions to two practical yet overlooked scenarios in federated learning for electronic health records (EHRs): firstly, we introduce EHRFL, a framework that facilitates federated learning across healthcare institutions with distinct medical coding systems and database schemas using text-based linearization of EHRs.

Federated Learning

TrustSQL: A Reliability Benchmark for Text-to-SQL Models with Diverse Unanswerable Questions

no code implementations23 Mar 2024 Gyubok Lee, Woosog Chay, Seonhee Cho, Edward Choi

To explore this aspect, we introduce TrustSQL, a new benchmark designed to assess the reliability of text-to-SQL models in both single-database and cross-database settings.


KorMedMCQA: Multi-Choice Question Answering Benchmark for Korean Healthcare Professional Licensing Examinations

no code implementations3 Mar 2024 Sunjun Kweon, Byungjin Choi, Minkyu Kim, Rae Woong Park, Edward Choi

We introduce KorMedMCQA, the first Korean multiple-choice question answering (MCQA) benchmark derived from Korean healthcare professional licensing examinations, covering from the year 2012 to year 2023.

Multiple-choice Multiple Choice Question Answering (MCQA)

EHRNoteQA: A Patient-Specific Question Answering Benchmark for Evaluating Large Language Models in Clinical Settings

1 code implementation25 Feb 2024 Sunjun Kweon, Jiyoun Kim, Heeyoung Kwak, Dongchul Cha, Hangyul Yoon, Kwanghyun Kim, Seunghyun Won, Edward Choi

This study introduces EHRNoteQA, a novel patient-specific question answering benchmark tailored for evaluating Large Language Models (LLMs) in clinical environments.

Decision Making Question Answering

Multimodal Transformer With a Low-Computational-Cost Guarantee

no code implementations23 Feb 2024 Sungjin Park, Edward Choi

Transformer-based models have significantly improved performance across a range of multimodal understanding tasks, such as visual question answering and action recognition.

Action Recognition Question Answering +1

KorNAT: LLM Alignment Benchmark for Korean Social Values and Common Knowledge

no code implementations21 Feb 2024 Jiyoung Lee, Minwoo Kim, Seungho Kim, Junghwan Kim, Seunghyun Won, Hwaran Lee, Edward Choi

For the common knowledge dataset, we constructed samples based on Korean textbooks and GED reference materials.

4k Multiple-choice

Self-Supervised Contrastive Learning for Long-term Forecasting

1 code implementation3 Feb 2024 Junwoo Park, Daehoon Gwak, Jaegul Choo, Edward Choi

To this end, our contrastive loss incorporates global autocorrelation held in the whole time series, which facilitates the construction of positive and negative pairs in a self-supervised manner.

Contrastive Learning Time Series +1

Learning under Label Noise through Few-Shot Human-in-the-Loop Refinement

no code implementations25 Jan 2024 Aaqib Saeed, Dimitris Spathis, JungWoo Oh, Edward Choi, Ali Etemad

We show that FHLR achieves significantly better performance when learning from noisy labels and achieves state-of-the-art by a large margin, with up to 19% accuracy improvement under symmetric and asymmetric noise.

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

KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models

1 code implementation17 Oct 2023 Jiho Kim, Yeonsu Kwon, Yohan Jo, Edward Choi

While large language models (LLMs) have made considerable advancements in understanding and generating unstructured text, their application in structured data remains underexplored.

Fact Verification Knowledge Graphs +3

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

Open-WikiTable: Dataset for Open Domain Question Answering with Complex Reasoning over Table

1 code implementation12 May 2023 Sunjun Kweon, Yeonsu Kwon, Seonhee Cho, Yohan Jo, Edward Choi

Despite recent interest in open domain question answering (ODQA) over tables, many studies still rely on datasets that are not truly optimal for the task with respect to utilizing structural nature of table.

Open-Domain Question Answering

FactKG: Fact Verification via Reasoning on Knowledge Graphs

1 code implementation11 May 2023 Jiho Kim, Sungjin Park, Yeonsu Kwon, Yohan Jo, James Thorne, Edward Choi

KGs can be a valuable knowledge source in fact verification due to their reliability and broad applicability.

Fact Verification Knowledge Graphs +1

Rediscovery of CNN's Versatility for Text-based Encoding of Raw Electronic Health Records

1 code implementation15 Mar 2023 Eunbyeol Cho, Min Jae Lee, Kyunghoon Hur, Jiyoun Kim, Jinsung Yoon, Edward Choi

Making the most use of abundant information in electronic health records (EHR) is rapidly becoming an important topic in the medical domain.

Perspective Projection-Based 3D CT Reconstruction from Biplanar X-rays

1 code implementation9 Mar 2023 Daeun Kyung, Kyungmin Jo, Jaegul Choo, Joonseok Lee, Edward Choi

X-ray computed tomography (CT) is one of the most common imaging techniques used to diagnose various diseases in the medical field.

Computed Tomography (CT)

Text-to-ECG: 12-Lead Electrocardiogram Synthesis conditioned on Clinical Text Reports

1 code implementation9 Mar 2023 Hyunseung Chung, Jiho Kim, Joon-Myoung Kwon, Ki-Hyun Jeon, Min Sung Lee, Edward Choi

We compare the performance of our model with other representative models in text-to-speech and text-to-image.

Deep Imbalanced Time-series Forecasting via Local Discrepancy Density

1 code implementation27 Feb 2023 Junwoo Park, Jungsoo Lee, Youngin Cho, Woncheol Shin, Dongmin Kim, Jaegul Choo, Edward Choi

Based on our findings, we propose a reweighting framework that down-weights the losses incurred by abrupt changes and up-weights those by normal states.

Time Series Time Series Forecasting

Vision-Language Generative Model for View-Specific Chest X-ray Generation

1 code implementation23 Feb 2023 Hyungyung Lee, Da Young Lee, Wonjae Kim, Jin-Hwa Kim, Tackeun Kim, Jihang Kim, Leonard Sunwoo, Edward Choi

Synthetic medical data generation has opened up new possibilities in the healthcare domain, offering a powerful tool for simulating clinical scenarios, enhancing diagnostic and treatment quality, gaining granular medical knowledge, and accelerating the development of unbiased algorithms.

Language Modelling Quantization

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

Significantly Improving Zero-Shot X-ray Pathology Classification via Fine-tuning Pre-trained Image-Text Encoders

no code implementations14 Dec 2022 Jongseong Jang, Daeun Kyung, Seung Hwan Kim, Honglak Lee, Kyunghoon Bae, Edward Choi

However, large-scale and high-quality data to train powerful neural networks are rare in the medical domain as the labeling must be done by qualified experts.

Classification Contrastive Learning +2

UniHPF : Universal Healthcare Predictive Framework with Zero Domain Knowledge

no code implementations15 Nov 2022 Kyunghoon Hur, JungWoo Oh, Junu Kim, Jiyoun Kim, Min Jae Lee, Eunbyeol Cho, Seong-Eun Moon, Young-Hak Kim, Edward Choi

Despite the abundance of Electronic Healthcare Records (EHR), its heterogeneity restricts the utilization of medical data in building predictive models.

Universal EHR Federated Learning Framework

1 code implementation14 Nov 2022 Junu Kim, Kyunghoon Hur, Seongjun Yang, Edward Choi

Federated learning (FL) is the most practical multi-source learning method for electronic healthcare records (EHR).

Federated Learning

Do Language Models Understand Measurements?

no code implementations23 Oct 2022 Sungjin Park, Seungwoo Ryu, Edward Choi

Recent success of pre-trained language models (PLMs) has stimulated interest in their ability to understand and work with numbers.

Language Modelling

Correlation between Alignment-Uniformity and Performance of Dense Contrastive Representations

1 code implementation17 Oct 2022 Jong Hak Moon, Wonjae Kim, Edward Choi

Recently, dense contrastive learning has shown superior performance on dense prediction tasks compared to instance-level contrastive learning.

Contrastive Learning Linear evaluation

Reweighting Strategy based on Synthetic Data Identification for Sentence Similarity

1 code implementation COLING 2022 Taehee Kim, ChaeHun Park, Jimin Hong, Radhika Dua, Edward Choi, Jaegul Choo

To analyze this, we first train a classifier that identifies machine-written sentences, and observe that the linguistic features of the sentences identified as written by a machine are significantly different from those of human-written sentences.

Sentence Sentence Embedding +2

Automatic Detection of Noisy Electrocardiogram Signals without Explicit Noise Labels

no code implementations8 Aug 2022 Radhika Dua, Jiyoung Lee, Joon-Myoung Kwon, Edward Choi

Automatic deep learning-based examination of ECG signals can lead to inaccurate diagnosis, and manual analysis involves rejection of noisy ECG samples by clinicians, which might cost extra time.

Task Agnostic and Post-hoc Unseen Distribution Detection

no code implementations26 Jul 2022 Radhika Dua, Seongjun Yang, Yixuan Li, Edward Choi

Despite the recent advances in out-of-distribution(OOD) detection, anomaly detection, and uncertainty estimation tasks, there do not exist a task-agnostic and post-hoc approach.

Anomaly Detection Out of Distribution (OOD) Detection

GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learning

2 code implementations20 Jul 2022 Kyunghoon Hur, JungWoo Oh, Junu Kim, Jiyoun Kim, Min Jae Lee, Eunbyeol Cho, Seong-Eun Moon, Young-Hak Kim, Louis Atallah, Edward Choi

To address this challenge, we propose General Healthcare Predictive Framework (GenHPF), which is applicable to any EHR with minimal preprocessing for multiple prediction tasks.

Feature Engineering Multi-Task Learning

Towards the Practical Utility of Federated Learning in the Medical Domain

1 code implementation7 Jul 2022 Seongjun Yang, Hyeonji Hwang, Daeyoung Kim, Radhika Dua, Jong-Yeup Kim, Eunho Yang, Edward Choi

We evaluate six FL algorithms designed for addressing data heterogeneity among clients, and a hybrid algorithm combining the strengths of two representative FL algorithms.

Federated Learning

Revisiting the Importance of Amplifying Bias for Debiasing

1 code implementation29 May 2022 Jungsoo Lee, Jeonghoon Park, Daeyoung Kim, Juyoung Lee, Edward Choi, Jaegul Choo

$f_B$ is trained to focus on bias-aligned samples (i. e., overfitted to the bias) while $f_D$ is mainly trained with bias-conflicting samples by concentrating on samples which $f_B$ fails to learn, leading $f_D$ to be less susceptible to the dataset bias.

Attribute Image Classification

Unconditional Image-Text Pair Generation with Multimodal Cross Quantizer

1 code implementation15 Apr 2022 Hyungyung Lee, Sungjin Park, Joonseok Lee, Edward Choi

To learn a multimodal semantic correlation in a quantized space, we combine VQ-VAE with a Transformer encoder and apply an input masking strategy.

multimodal generation Quantization

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

Lead-agnostic Self-supervised Learning for Local and Global Representations of Electrocardiogram

1 code implementation14 Mar 2022 JungWoo Oh, Hyunseung Chung, Joon-Myoung Kwon, Dong-gyun Hong, Edward Choi

In this work, we propose an ECG pre-training method that learns both local and global contextual representations for better generalizability and performance on downstream tasks.

Self-Supervised Learning

Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting

1 code implementation21 Jan 2022 Kwanhyung Lee, Hyewon Jeong, Seyun Kim, Donghwa Yang, Hoon-Chul Kang, Edward Choi

Electroencephalogram (EEG) is an important diagnostic test that physicians use to record brain activity and detect seizures by monitoring the signals.

EEG Seizure Detection

Exploration into Translation-Equivariant Image Quantization

2 code implementations1 Dec 2021 Woncheol Shin, Gyubok Lee, Jiyoung Lee, Eunyi Lyou, Joonseok Lee, Edward Choi

This is an exploratory study that discovers the current image quantization (vector quantization) do not satisfy translation equivariance in the quantized space due to aliasing.

Quantization Text Generation +2

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

Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding

1 code implementation12 Nov 2021 Kyunghoon Hur, Jiyoung Lee, JungWoo Oh, Wesley Price, Young-Hak Kim, Edward Choi

EHR systems lack a unified code system forrepresenting medical concepts, which acts asa barrier for the deployment of deep learningmodels in large scale to multiple clinics and hos-pitals.

Representation Learning

Natural Attribute-based Shift Detection

no code implementations18 Oct 2021 Jeonghoon Park, Jimin Hong, Radhika Dua, Daehoon Gwak, Yixuan Li, Jaegul Choo, Edward Choi

Despite the impressive performance of deep networks in vision, language, and healthcare, unpredictable behaviors on samples from the distribution different than the training distribution cause severe problems in deployment.

Attribute Out of Distribution (OOD) Detection

Decoupled Kernel Neural Processes: Neural Network-Parameterized Stochastic Processes using Explicit Data-driven Kernel

no code implementations29 Sep 2021 Daehoon Gwak, Gyubok Lee, Jaehoon Lee, Jaesik Choi, Jaegul Choo, Edward Choi

To address this, we introduce a new neural stochastic processes, Decoupled Kernel Neural Processes (DKNPs), which explicitly learn a separate mean and kernel function to directly model the covariance between output variables in a data-driven manner.

Gaussian Processes

Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding

1 code implementation8 Aug 2021 Kyunghoon Hur, Jiyoung Lee, JungWoo Oh, Wesley Price, Young-Hak Kim, Edward Choi

To overcome this problem, we introduce Description-based Embedding, DescEmb, a code-agnostic description-based representation learning framework for predictive modeling on EHR.

Representation Learning Transfer Learning

Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training

1 code implementation24 May 2021 Jong Hak Moon, Hyungyung Lee, Woncheol Shin, Young-Hak Kim, Edward Choi

Recently a number of studies demonstrated impressive performance on diverse vision-language multi-modal tasks such as image captioning and visual question answering by extending the BERT architecture with multi-modal pre-training objectives.

Image Captioning Medical Visual Question Answering +6

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

1 code implementation ACL 2021 Gyubok Lee, Seongjun Yang, Edward Choi

Accurate terminology translation is crucial for ensuring the practicality and reliability of neural machine translation (NMT) systems.

Machine Translation NMT +2

A System for Automated Open-Source Threat Intelligence Gathering and Management

no code implementations19 Jan 2021 Peng Gao, Xiaoyuan Liu, Edward Choi, Bhavna Soman, Chinmaya Mishra, Kate Farris, Dawn Song

SecurityKG collects OSCTI reports from various sources, uses a combination of AI and NLP techniques to extract high-fidelity knowledge about threat behaviors, and constructs a security knowledge graph.


Neural Ordinary Differential Equations for Intervention Modeling

1 code implementation16 Oct 2020 Daehoon Gwak, Gyuhyeon Sim, Michael Poli, Stefano Massaroli, Jaegul Choo, Edward Choi

By interpreting the forward dynamics of the latent representation of neural networks as an ordinary differential equation, Neural Ordinary Differential Equation (Neural ODE) emerged as an effective framework for modeling a system dynamics in the continuous time domain.

Time Series Time Series Analysis

Vid-ODE: Continuous-Time Video Generation with Neural Ordinary Differential Equation

1 code implementation16 Oct 2020 Sunghyun Park, Kangyeol Kim, Junsoo Lee, Jaegul Choo, Joonseok Lee, Sookyung Kim, Edward Choi

Video generation models often operate under the assumption of fixed frame rates, which leads to suboptimal performance when it comes to handling flexible frame rates (e. g., increasing the frame rate of the more dynamic portion of the video as well as handling missing video frames).

Decoder Video Generation

Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer

2 code implementations11 Jun 2019 Edward Choi, Zhen Xu, Yujia Li, Michael W. Dusenberry, Gerardo Flores, Yuan Xue, Andrew M. Dai

A recent study showed that using the graphical structure underlying EHR data (e. g. relationship between diagnoses and treatments) improves the performance of prediction tasks such as heart failure prediction.

Graph Reconstruction Readmission Prediction +1

Analyzing the Role of Model Uncertainty for Electronic Health Records

1 code implementation10 Jun 2019 Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine Heller, Andrew M. Dai

We further show that RNNs with only Bayesian embeddings can be a more efficient way to capture model uncertainty compared to ensembles, and we analyze how model uncertainty is impacted across individual input features and patient subgroups.

MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare

1 code implementation NeurIPS 2018 Edward Choi, Cao Xiao, Walter F. Stewart, Jimeng Sun

Deep learning models exhibit state-of-the-art performance for many predictive healthcare tasks using electronic health records (EHR) data, but these models typically require training data volume that exceeds the capacity of most healthcare systems.

Disease Prediction

RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records

no code implementations28 May 2018 Bum Chul Kwon, Min-Je Choi, Joanne Taery Kim, Edward Choi, Young Bin Kim, Soonwook Kwon, Jimeng Sun, Jaegul Choo

Therefore, our design study aims to provide a visual analytics solution to increase interpretability and interactivity of RNNs via a joint effort of medical experts, artificial intelligence scientists, and visual analytics researchers.

Compositional Obverter Communication Learning From Raw Visual Input

2 code implementations ICLR 2018 Edward Choi, Angeliki Lazaridou, Nando de Freitas

Previously, it has been shown that neural network agents can learn to communicate in a highly structured, possibly compositional language based on disentangled input (e. g. hand- engineered features).

Causal Regularization

no code implementations8 Feb 2017 Mohammad Taha Bahadori, Krzysztof Chalupka, Edward Choi, Robert Chen, Walter F. Stewart, Jimeng Sun

In application domains such as healthcare, we want accurate predictive models that are also causally interpretable.

Representation Learning

GRAM: Graph-based Attention Model for Healthcare Representation Learning

1 code implementation21 Nov 2016 Edward Choi, Mohammad Taha Bahadori, Le Song, Walter F. Stewart, Jimeng Sun

-Interpretation:The representations learned by deep learning methods should align with medical knowledge.

Representation Learning

RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism

1 code implementation NeurIPS 2016 Edward Choi, Mohammad Taha Bahadori, Joshua A. Kulas, Andy Schuetz, Walter F. Stewart, Jimeng Sun

RETAIN was tested on a large health system EHR dataset with 14 million visits completed by 263K patients over an 8 year period and demonstrated predictive accuracy and computational scalability comparable to state-of-the-art methods such as RNN, and ease of interpretability comparable to traditional models.

Disease Trajectory Forecasting

Multi-layer Representation Learning for Medical Concepts

2 code implementations17 Feb 2016 Edward Choi, Mohammad Taha Bahadori, Elizabeth Searles, Catherine Coffey, Jimeng Sun

Learning efficient representations for concepts has been proven to be an important basis for many applications such as machine translation or document classification.

Document Classification Machine Translation +3

Medical Concept Representation Learning from Electronic Health Records and its Application on Heart Failure Prediction

1 code implementation11 Feb 2016 Edward Choi, Andy Schuetz, Walter F. Stewart, Jimeng Sun

Objective: To transform heterogeneous clinical data from electronic health records into clinically meaningful constructed features using data driven method that rely, in part, on temporal relations among data.

Representation Learning

Doctor AI: Predicting Clinical Events via Recurrent Neural Networks

1 code implementation18 Nov 2015 Edward Choi, Mohammad Taha Bahadori, Andy Schuetz, Walter F. Stewart, Jimeng Sun

Leveraging large historical data in electronic health record (EHR), we developed Doctor AI, a generic predictive model that covers observed medical conditions and medication uses.

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