Search Results for author: Edward Choi

Found 54 papers, 31 papers with code

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

no code implementations15 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.

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.

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)

Deep Imbalanced Time-series Forecasting via Local Discrepancy Density

no code implementations27 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 Forecasting

Unified Chest X-ray and Radiology Report Generation Model with Multi-view Chest X-rays

no code implementations23 Feb 2023 Hyungyung Lee, Da Young Lee, Wonjae Kim, Jin-Hwa Kim, Tackeun Kim, Jihang Kim, Leonard Sunwoo, Edward Choi

We also find that view-specific special tokens can distinguish between different views and properly generate specific views even if they do not exist in the dataset, and utilizing multi-view chest X-rays can faithfully capture the abnormal findings in the additional X-rays.


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 +1

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

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 Embedding Sentence-Embedding +1

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

UniHPF : Universal Healthcare Predictive Framework with Zero Domain Knowledge

1 code implementation20 Jul 2022 Kyunghoon Hur, JungWoo Oh, Junu Kim, Min Jae Lee, Eunbyeol Cho, Jiyoun Kim, Seong-Eun Moon, Young-Hak Kim, Edward Choi

Experimental results demonstrate that UniHPF is capable of building large-scale EHR models that can process any form of medical data from distinct EHR systems.

Revisiting the Importance of Amplifying Bias for Debiasing

no code implementations29 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.

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

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

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

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.

Natural Questions Question Answering

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.

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 +1

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 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).

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).

Generating Multi-label Discrete Patient Records using Generative Adversarial Networks

3 code implementations19 Mar 2017 Edward Choi, Siddharth Biswal, Bradley Malin, Jon Duke, Walter F. Stewart, Jimeng Sun

Access to electronic health record (EHR) data has motivated computational advances in medical research.

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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