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

9. 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

10. 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

20. 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
2024
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25. 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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44. Microbiologic Profile of Nipple Swab Culture and its Association With Postoperative Complications in Prosthetic Breast Reconstruction.

Author

Moon, Yi-Jun, Chung, Jae-Ho, Lee, Hyung-Chul, Jung, Seung-Pil, and Yoon, Eul-Sik

Abstract

Background In breast surgeries, a lactiferous duct leading to lactic glands of breast parenchyma allows direct contamination by normal bacterial flora of the nipple-areola complex. Complete blockage of nipple flora from the intraoperative field is almost impossible. Objectives We aimed to analyze the microbiological profile of nipple flora of breast cancer patients who underwent an implant-based immediate breast reconstruction after a total mastectomy, and to evaluate the association of nipple bacterial flora with postoperative complications. Methods A retrospective chart review was performed of patients who underwent an implant-based immediate breast reconstruction after a total mastectomy. A nipple swab culture was performed preoperatively. Patient demographics, surgical characteristics, and complications were compared between positive and negative nipple swab culture groups. Microbiological profile data including antibacterial resistance were collected. Results Among 128 breasts, 60 cases (46.9%) had positive preoperative nipple swab culture results. Staphylococcus epidermidis accounted for 41.4% of microorganisms isolated. A multivariate logistic regression analysis of postoperative complications revealed that the presence of nipple bacterial flora was a risk factor for capsular contracture. Seven cases of postoperative infection were analyzed. In 2 cases (40% of pathogen-proven infection), the causative pathogen matched the patient's nipple bacterial flora, which was methicillin-resistant S. epidermidis in both cases. Conclusions Nipple bacterial flora was associated with an increased risk of capsular contracture. Preoperative analysis of nipple bacterial flora can be an informative source for treating clinically diagnosed postoperative infections. More studies are needed to determine the effectiveness of active antibiotic decolonization of the nipple. Level of Evidence: 3 [ABSTRACT FROM AUTHOR]

Published
2024
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