Hyung-Chul Lee
Orcid: 0000-0003-0048-7958
According to our database1,
Hyung-Chul Lee
authored at least 13 papers
between 2020 and 2024.
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Bibliography
2024
Comparison of NLP machine learning models with human physicians for ASA Physical Status classification.
npj Digit. Medicine, 2024
Reinforcement learning model for optimizing dexmedetomidine dosing to prevent delirium in critically ill patients.
npj Digit. Medicine, 2024
Non-invasive prediction of massive transfusion during surgery using intraoperative hemodynamic monitoring data.
J. Biomed. Informatics, 2024
Unmasking Societal Biases in Respiratory Support for ICU Patients through Social Determinants of Health.
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024
2023
Automatic segmentation of atrial fibrillation and flutter in single-lead electrocardiograms by self-supervised learning and Transformer architecture.
J. Am. Medical Informatics Assoc., December, 2023
Deep reinforcement learning-based propofol infusion control for anesthesia: A feasibility study with a 3000-subject dataset.
Comput. Biol. Medicine, April, 2023
Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICU.
npj Digit. Medicine, 2023
Development and validation of a reinforcement learning model for ventilation control during emergence from general anesthesia.
npj Digit. Medicine, 2023
2022
Multi-center validation of machine learning model for preoperative prediction of postoperative mortality.
npj Digit. Medicine, 2022
Developing and Validating Multi-Modal Models for Mortality Prediction in COVID-19 Patients: a Multi-center Retrospective Study.
J. Digit. Imaging, 2022
Attention Mechanisms for Physiological Signal Deep Learning: Which Attention Should We Take?
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022
2020
A Real-Time Depth of Anesthesia Monitoring System Based on Deep Neural Network With Large EDO Tolerant EEG Analog Front-End.
IEEE Trans. Biomed. Circuits Syst., 2020
A Deep Learning Method for Intraoperative Age-agnostic and Disease-specific Cardiac Output Monitoring from Arterial Blood Pressure.
Proceedings of the 20th IEEE International Conference on Bioinformatics and Bioengineering, 2020