no code implementations • 20 Feb 2025 • Sujeong Im, JungWoo Oh, Edward Choi
Lab tests are fundamental for diagnosing diseases and monitoring patient conditions.
1 code implementation • 18 Feb 2025 • Sumin Jo, Junseong Choi, Jiho Kim, Edward Choi
To address this, We introduce R2-KG, a plug-and-play, dual-agent framework that separates reasoning into two roles: an Operator (a low-capacity LLM) that gathers evidence and a Supervisor (a high-capacity LLM) that makes final judgments.
no code implementations • 20 Dec 2024 • Sungjin Park, Xiao Liu, Yeyun Gong, Edward Choi
In response, we present Language model Ensemble with Monte Carlo Tree Search (LE-MCTS), a novel framework for process-level ensembling of language models.
1 code implementation • 21 Nov 2024 • Daehoon Gwak, Junwoo Park, Minho Park, ChaeHun Park, Hyunchan Lee, Edward Choi, Jaegul Choo
Predicting future international events from textual information, such as news articles, has tremendous potential for applications in global policy, strategic decision-making, and geopolitics.
no code implementations • 13 Oct 2024 • Soyoung Yang, Hojun Cho, Jiyoung Lee, Sohee Yoon, Edward Choi, Jaegul Choo, Won Ik Cho
Aspect-based sentiment analysis (ABSA) is the challenging task of extracting sentiment along with its corresponding aspects and opinions from human language.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
no code implementations • 11 Sep 2024 • Daeun Kyung, Junu Kim, Tackeun Kim, Edward Choi
Chest X-ray imaging (CXR) is an important diagnostic tool used in hospitals to assess patient conditions and monitor changes over time.
1 code implementation • 7 Aug 2024 • Hyunseung Chung, Sumin Jo, Yeonsu Kwon, Edward Choi
We conduct extensive experiments in the generated synthetic dataset and various UCR Time-Series datasets to first compare the explanation performance of FIA and other existing perturbation-based XAI methods in both time-domain and time-frequency domain, and then show the superiority of our FIA in the time-frequency domain with the SpectralX framework.
1 code implementation • 24 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.
1 code implementation • 19 Jun 2024 • Jiho Kim, Woosog Chay, Hyeonji Hwang, Daeun Kyung, Hyunseung Chung, Eunbyeol Cho, Yohan Jo, Edward Choi
Recent advancements in Large Language Models (LLMs) have significantly enhanced the capabilities of conversational agents, making them applicable to various fields (e. g., education).
1 code implementation • 23 May 2024 • Jaehee Ryu, Seonhee Cho, Gyubok Lee, Edward Choi
In this paper, we introduce EHR-SeqSQL, a novel sequential text-to-SQL dataset for Electronic Health Record (EHR) databases.
1 code implementation • 4 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.
no code implementations • 29 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.
1 code implementation • 20 Apr 2024 • Jiyoun Kim, Junu Kim, Kyunghoon Hur, Edward Choi
The increasing volume of electronic health records (EHRs) across healthcare institutions presents the opportunity to enhance model accuracy and robustness in clinical prediction tasks.
1 code implementation • 23 Mar 2024 • Gyubok Lee, Woosog Chay, Seonhee Cho, Edward Choi
To enable wider deployment, it is crucial to address these challenges in model design and enhance model evaluation to build trust in the model's output.
no code implementations • 3 Mar 2024 • Hyewon Jeong, Sarah Jabbour, Yuzhe Yang, Rahul Thapta, Hussein Mozannar, William Jongwon Han, Nikita Mehandru, Michael Wornow, Vladislav Lialin, Xin Liu, Alejandro Lozano, Jiacheng Zhu, Rafal Dariusz Kocielnik, Keith Harrigian, Haoran Zhang, Edward Lee, Milos Vukadinovic, Aparna Balagopalan, Vincent Jeanselme, Katherine Matton, Ilker Demirel, Jason Fries, Parisa Rashidi, Brett Beaulieu-Jones, Xuhai Orson Xu, Matthew McDermott, Tristan Naumann, Monica Agrawal, Marinka Zitnik, Berk Ustun, Edward Choi, Kristen Yeom, Gamze Gursoy, Marzyeh Ghassemi, Emma Pierson, George Chen, Sanjat Kanjilal, Michael Oberst, Linying Zhang, Harvineet Singh, Tom Hartvigsen, Helen Zhou, Chinasa T. Okolo
The organization of the research roundtables at the conference involved 17 Senior Chairs and 19 Junior Chairs across 11 tables.
no code implementations • 3 Mar 2024 • Sunjun Kweon, Byungjin Choi, Gyouk Chu, Junyeong Song, Daeun Hyeon, Sujin Gan, Jueon Kim, Minkyu Kim, Rae Woong Park, Edward Choi
We present KorMedMCQA, the first Korean Medical Multiple-Choice Question Answering benchmark, derived from professional healthcare licensing examinations conducted in Korea between 2012 and 2024.
1 code implementation • Neural Information Processing Systems (NeurIPS) 2024 • Sunjun Kweon, Jiyoun Kim, Heeyoung Kwak, Dongchul Cha, Hangyul Yoon, Kwanghyun Kim, Jeewon Yang, Seunghyun Won, Edward Choi
Furthermore, to validate EHRNoteQA as a reliable proxy for expert evaluations in clinical practice, we measure the correlation between the LLM performance on EHRNoteQA, and the LLM performance manually evaluated by clinicians.
no code implementations • 23 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.
no code implementations • 21 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.
1 code implementation • 3 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.
Ranked #1 on
Time Series Forecasting
on ETTh1 (720) Univariate
no code implementations • 25 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.
1 code implementation • 31 Oct 2023 • Junu Kim, Chaeeun Shim, Bosco Seong Kyu Yang, Chami Im, Sung Yoon Lim, Han-Gil Jeong, Edward Choi
This allows for an unrestricted input size, eliminating the need for manual event selection.
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.
1 code implementation • 17 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.
1 code implementation • 1 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.
1 code implementation • 3 Aug 2023 • Jiyoung Lee, Seungho Kim, Seunghyun Won, Joonseok Lee, Marzyeh Ghassemi, James Thorne, Jaeseok Choi, O-Kil Kwon, Edward Choi
In this paper, we focus on the models' visual perception alignment with humans, further referred to as AI-human visual alignment.
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.
1 code implementation • 12 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.
1 code implementation • 11 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.
no code implementations • 4 May 2023 • Kwanhyung Lee, Soojeong Lee, Sangchul Hahn, Heejung Hyun, Edward Choi, Byungeun Ahn, Joohyung Lee
Electronic Health Record (EHR) provides abundant information through various modalities.
1 code implementation • 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 • 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 • 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 • 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.
1 code implementation • 23 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.
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.
no code implementations • 14 Dec 2022 • Jongseong Jang, Daeun Kyung, Seung Hwan Kim, Honglak Lee, Kyunghoon Bae, Edward Choi
Deep neural networks are increasingly used in medical imaging for tasks such as pathological classification, but they face challenges due to the scarcity of high-quality, expert-labeled training data.
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
Accurate time prediction of patients' critical events is crucial in urgent scenarios where timely decision-making is important.
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.
2 code implementations • 20 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.
1 code implementation • 7 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.
1 code implementation • 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 • 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).
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.
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.