no code implementations • 15 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.
1 code implementation • 9 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.
1 code implementation • 9 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.
no code implementations • 27 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.
no code implementations • 23 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.
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
The utterances were collected from 222 hospital staff, including physicians, nurses, insurance review and health records teams, and more.
no code implementations • 14 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.
no code implementations • 15 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.
1 code implementation • 14 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).
no code implementations • 29 Oct 2022 • Kwanhyung Lee, John Won, Heejung Hyun, Sangchul Hahn, Edward Choi, Joohyung Lee
Therefore, in this study, we propose an hourly prediction method of critical events in ED, i. e., mortality and vasopressor need.
no code implementations • 24 Oct 2022 • Jiyoung Lee, Hantae Kim, Hyunchang Cho, Edward Choi, Cheonbok Park
Multi-domain Neural Machine Translation (NMT) trains a single model with multiple domains.
no code implementations • 23 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.
1 code implementation • 17 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.
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.
no code implementations • 8 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.
no code implementations • 26 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.
1 code implementation • 20 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.
no code implementations • 7 Jul 2022 • Seongjun Yang, Hyeonji Hwang, Daeyoung Kim, Radhika Dua, Jong-Yeup Kim, Eunho Yang, Edward Choi
Federated learning (FL) is an active area of research.
no code implementations • 29 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.
1 code implementation • 15 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.
1 code implementation • 18 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.
1 code implementation • 14 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.
1 code implementation • 14 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.
1 code implementation • 21 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.
Ranked #1 on
Seizure Detection
on TUH EEG Seizure Corpus
no code implementations • 21 Dec 2021 • Kangyeol Kim, Sunghyun Park, Junsoo Lee, Joonseok Lee, Sookyung Kim, Jaegul Choo, Edward Choi
In order to perform unconditional video generation, we must learn the distribution of the real-world videos.
2 code implementations • 1 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.
no code implementations • 14 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.
1 code implementation • 12 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.
no code implementations • 24 Oct 2021 • Jiyoung Lee, Wonjae Kim, Daehoon Gwak, Edward Choi
Periodic signals play an important role in daily lives.
no code implementations • 18 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.
no code implementations • 29 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.
1 code implementation • 8 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.
1 code implementation • 24 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.
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.
no code implementations • 19 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.
no code implementations • 26 Nov 2020 • Jeonghoon Park, Kyungmin Jo, Daehoon Gwak, Jimin Hong, Jaegul Choo, Edward Choi
We evaluate the out-of-distribution (OOD) detection performance of self-supervised learning (SSL) techniques with a new evaluation framework.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+1
1 code implementation • 19 Oct 2020 • Junwoo Park, Youngwoo Cho, Haneol Lee, Jaegul Choo, Edward Choi
Question Answering (QA) is a widely-used framework for developing and evaluating an intelligent machine.
1 code implementation • 16 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.
1 code implementation • 16 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).
2 code implementations • 11 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.
1 code implementation • 10 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.
no code implementations • WS 2019 • Sarah Wiegreffe, Edward Choi, Sherry Yan, Jimeng Sun, Jacob Eisenstein
The text of clinical notes can be a valuable source of patient information and clinical assessments.
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.
no code implementations • 28 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.
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).
3 code implementations • CVPR 2018 • Ariel Gordon, Elad Eban, Ofir Nachum, Bo Chen, Hao Wu, Tien-Ju Yang, Edward Choi
We present MorphNet, an approach to automate the design of neural network structures.
3 code implementations • 19 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.
no code implementations • 8 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.
1 code implementation • 21 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.
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.
Ranked #2 on
Disease Trajectory Forecasting
on UK CF trust
2 code implementations • 17 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.
1 code implementation • 11 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.
1 code implementation • 18 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.