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755 results on '"Lee, Hyung-Chul"'

1. NSTRI Global Collaborative Research Data Platform

Author

Lee, Hyeonhoon, Kim, Hanseul, Cho, Kyungmin, and Lee, Hyung-Chul

Subjects
Computer Science - Computers and Society, Computer Science - Information Retrieval
Abstract

The National Strategic Technology Research Institute (NSTRI) Data Platform operated by Seoul National University Hospital (SNUH) addresses the challenge of accessing Korean healthcare data for international research. This platform provides secure access to pseudonymized Korean healthcare data while integrating international datasets, enabling the development of more equitable and generalizable machine learning models. The system features four key AI-powered components: an intelligent data search engine utilizing domain-specific medical embeddings, a Korean-English medical translation system, a comprehensive drug search engine, and an LLM-powered medical research assistant. The platform implements containerized environments within a secure research pod architecture, ensuring data protection while maintaining research efficiency. The platform currently provides access to 10 distinct medical datasets from SNUH, categorized by access permissions and standardized for cross-dataset analysis. This infrastructure enables global collaborative healthcare research while maintaining strict data protection standards.

Published
2024

11. Attention mechanisms for physiological signal deep learning: which attention should we take?

Author

Park, Seong-A, Lee, Hyung-Chul, Jung, Chul-Woo, and Yang, Hyun-Lim

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Attention mechanisms are widely used to dramatically improve deep learning model performance in various fields. However, their general ability to improve the performance of physiological signal deep learning model is immature. In this study, we experimentally analyze four attention mechanisms (e.g., squeeze-and-excitation, non-local, convolutional block attention module, and multi-head self-attention) and three convolutional neural network (CNN) architectures (e.g., VGG, ResNet, and Inception) for two representative physiological signal prediction tasks: the classification for predicting hypotension and the regression for predicting cardiac output (CO). We evaluated multiple combinations for performance and convergence of physiological signal deep learning model. Accordingly, the CNN models with the spatial attention mechanism showed the best performance in the classification problem, whereas the channel attention mechanism achieved the lowest error in the regression problem. Moreover, the performance and convergence of the CNN models with attention mechanisms were better than stand-alone self-attention models in both problems. Hence, we verified that convolutional operation and attention mechanisms are complementary and provide faster convergence time, despite the stand-alone self-attention models requiring fewer parameters.

Published
2022

13. A machine learning model to predict liver-related outcomes after the functional cure of chronic hepatitis B

Author

Hur, Moon Haeng, Yip, Terry Cheuk-Fung, Kim, Seung Up, Lee, Hyun Woong, Lee, Han Ah, Lee, Hyung-Chul, Wong, Grace Lai-Hung, Wong, Vincent Wai-Sun, Park, Jun Yong, Ahn, Sang Hoon, Kim, Beom Kyung, Kim, Hwi Young, Seo, Yeon Seok, Shin, Hyunjae, Park, Jeayeon, Ko, Yunmi, Park, Youngsu, Lee, Yun Bin, Yu, Su Jong, Lee, Sang Hyub, Kim, Yoon Jun, Yoon, Jung-Hwan, and Lee, Jeong-Hoon

Published
2025
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15. Developing and validating multi-modal models for mortality prediction in COVID-19 patients: a multi-center retrospective study

Author

Wu, Joy Tzung-yu, de la Hoz, Miguel Ángel Armengol, Kuo, Po-Chih, Paguio, Joseph Alexander, Yao, Jasper Seth, Dee, Edward Christopher, Yeung, Wesley, Jurado, Jerry, Moulick, Achintya, Milazzo, Carmelo, Peinado, Paloma, Villares, Paula, Cubillo, Antonio, Varona, José Felipe, Lee, Hyung-Chul, Estirado, Alberto, Castellano, José Maria, and Celi, Leo Anthony

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

The unprecedented global crisis brought about by the COVID-19 pandemic has sparked numerous efforts to create predictive models for the detection and prognostication of SARS-CoV-2 infections with the goal of helping health systems allocate resources. Machine learning models, in particular, hold promise for their ability to leverage patient clinical information and medical images for prediction. However, most of the published COVID-19 prediction models thus far have little clinical utility due to methodological flaws and lack of appropriate validation. In this paper, we describe our methodology to develop and validate multi-modal models for COVID-19 mortality prediction using multi-center patient data. The models for COVID-19 mortality prediction were developed using retrospective data from Madrid, Spain (N=2547) and were externally validated in patient cohorts from a community hospital in New Jersey, USA (N=242) and an academic center in Seoul, Republic of Korea (N=336). The models we developed performed differently across various clinical settings, underscoring the need for a guided strategy when employing machine learning for clinical decision-making. We demonstrated that using features from both the structured electronic health records and chest X-ray imaging data resulted in better 30-day-mortality prediction performance across all three datasets (areas under the receiver operating characteristic curves: 0.85 (95% confidence interval: 0.83-0.87), 0.76 (0.70-0.82), and 0.95 (0.92-0.98)). We discuss the rationale for the decisions made at every step in developing the models and have made our code available to the research community. We employed the best machine learning practices for clinical model development. Our goal is to create a toolkit that would assist investigators and organizations in building multi-modal models for prediction, classification and/or optimization.

Published
2021

28. Developing and Validating Multi-Modal Models for Mortality Prediction in COVID-19 Patients: a Multi-center Retrospective Study

Author

Wu, Joy Tzung-yu, de la Hoz, Miguel Ángel Armengol, Kuo, Po-Chih, Paguio, Joseph Alexander, Yao, Jasper Seth, Dee, Edward Christopher, Yeung, Wesley, Jurado, Jerry, Moulick, Achintya, Milazzo, Carmelo, Peinado, Paloma, Villares, Paula, Cubillo, Antonio, Varona, José Felipe, Lee, Hyung-Chul, Estirado, Alberto, Castellano, José Maria, and Celi, Leo Anthony

Published
2022
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31. An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B

Author

Kim, Hwi Young, Lampertico, Pietro, Nam, Joon Yeul, Lee, Hyung-Chul, Kim, Seung Up, Sinn, Dong Hyun, Seo, Yeon Seok, Lee, Han Ah, Park, Soo Young, Lim, Young-Suk, Jang, Eun Sun, Yoon, Eileen L., Kim, Hyoung Su, Kim, Sung Eun, Ahn, Sang Bong, Shim, Jae-Jun, Jeong, Soung Won, Jung, Yong Jin, Sohn, Joo Hyun, Cho, Yong Kyun, Jun, Dae Won, Dalekos, George N., Idilman, Ramazan, Sypsa, Vana, Berg, Thomas, Buti, Maria, Calleja, Jose Luis, Goulis, John, Manolakopoulos, Spilios, Janssen, Harry L.A., Jang, Myoung-jin, Lee, Yun Bin, Kim, Yoon Jun, Yoon, Jung-Hwan, Papatheodoridis, George V., and Lee, Jeong-Hoon

Published
2022
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