Hyun-Lim Yang

Research Assistant Professor

Seoul National University Hospital

South Korea

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Dr. Hyun-Lim Yang is a Research Assistant Professor at Seoul National University Hospital in South Korea. His research focuses on advanced predictive modeling and machine learning applications in healthcare, particularly in cardiovascular health, chronic pain management, and optical coherence tomography. Recent articles authored by Dr. Yang explore risk prediction models for chronic opioid use after surgery and the use of deep learning to optimize patient care in critical settings.

Articles (15)

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2025

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2025

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2025

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2025

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2025

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2024

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2024

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2023

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2023

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2023

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2023

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2022

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2022

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2021

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2021

Collaborators (10)

H.‐K. Yoon

SOUTH KOREA

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Jonghae Kim

Professor

Catholic University of Korea School of Medicine

SOUTH KOREA

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KYUNAM KIM

Hanyang University Guri Hospital

SOUTH KOREA

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Kyungsu Lee

Assistant Professor

Jeju National University

SOUTH KOREA

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Jung-Bin Park

Seoul National University Hospital

SOUTH KOREA

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Hyeonhoon Lee

Kyungpook National University

SOUTH KOREA

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Eugene Kim

Associate professor

Hanyang University College of Medicine

SOUTH KOREA

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Chul-Woo Jung

Associate Professor

Seoul National University Hospital

SOUTH KOREA

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Hong Yeul Lee

Seoul National University Hospital

SOUTH KOREA

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Hyung-Chul Lee

Seoul National University

SOUTH KOREA

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Hyun-Lim Yang

Research Interests

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Articles (15)

Analysis and development of risk prediction models for chronic opioid use after surgery: a cohort study using the nationwide database

The effect of selection bias on the performance of a deep learning-based intraoperative hypotension prediction model using real-world samples from a publicly available database

Development and validation of a multi-stage self-supervised learning model for optical coherence tomography image classification

Feasibility of estimating tidal volume from electrocardiograph-derived respiration signal and respiration waveform

Prediction of reduced left ventricular ejection fraction using atrial fibrillation or flutter electrocardiograms: A machine-learning study

Deep learning-based long-term risk evaluation of incident type 2 diabetes using electrocardiogram in a non-diabetic population: a retrospective, multicentre study

Reinforcement learning model for optimizing dexmedetomidine dosing to prevent delirium in critically ill patients

Machine learning-based prediction of intraoperative hypoxemia for pediatric patients

Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICU

Automatic segmentation of atrial fibrillation and flutter in single-lead electrocardiograms by self-supervised learning and Transformer architecture

Real-time dual prediction of intradialytic hypotension and hypertension using an explainable deep learning model

Deep‐learning model associating lateral cervical radiographic features with <scp>Cormack–Lehane</scp> grade 3 or 4 glottic view

Artificial intelligence in perioperative medicine: a narrative review

Development and Validation of an Arterial Pressure-Based Cardiac Output Algorithm Using a Convolutional Neural Network: Retrospective Study Based on Prospective Registry Data

Automatic prediction of left cardiac chamber enlargement from chest radiographs using convolutional neural network

Collaborators (10)

H.‐K. Yoon

Jonghae Kim

KYUNAM KIM

Kyungsu Lee

Jung-Bin Park

Hyeonhoon Lee

Eugene Kim

Chul-Woo Jung

Hong Yeul Lee

Hyung-Chul Lee

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