Kyung Hyun Lee

Seoul & Suwon

Republic of Korea

lkh256 [at] gmail [dot] com

I’m a PhD student in Digital Health at SAIHST, Sungkyunkwan University, advised by Professor Byung-Jae Lee, and an AI researcher at AITRICS.

My work sits between machine learning research and clinical deployment. I like thinking about how a model becomes a medical device — how it’s trained, how it’s validated, and how it earns regulatory trust.

At AITRICS, I contributed to a cardiac arrest early-warning system that was approved by Korea’s MFDS as an AI medical device. I’m now leading the product development of an AKI prediction model I developed, with the same goal of MFDS approval.

For my doctoral research, I work with longitudinal pulmonary function data from Samsung Medical Center, building time-series models to better capture how respiratory health changes over time.

In 2025, I co-founded BreathYou, a digital health startup focused on allergy and respiratory AI that turns parts of this research into products clinicians can use.

I’m always happy to talk with people working on clinical AI, medical device development, or respiratory health — feel free to reach out.

news

May 08, 2026	Our paper on deep learning models for AKI prediction — multi-center external validation and evaluation under simulated continuous monitoring conditions — has been published in *npj Digital Medicine*.
Apr 29, 2026	Our paper on deep learning models for AKI prediction — multi-center external validation and evaluation under simulated continuous monitoring conditions — has been accepted in *npj Digital Medicine*.
Mar 30, 2026	Presented an oral poster, “Online Simulation Versus Single-Point Evaluation for AKI Prediction: Identifying Optimal Prediction Horizons for Continuous Monitoring”, at AKI & CRRT 2026 — part of the work submitted to npj Digital Medicine.
Feb 23, 2026	Our multicenter study on deep learning models for acute kidney injury prediction, including development and external validation, has been submitted to npj Digital Medicine and is currently under review.

selected publications

Deep learning models for acute kidney injury prediction: multi-center external validation and evaluation under simulated continuous monitoring conditions

Kyung Hyun Lee^†, Donghwee Yoon^†, Hyunsun Lim, and 2 more authors

npj Digital Medicine, May 2026

DOI
Electromyogram-based classification of hand and finger gestures using artificial neural networks

Kyung Hyun Lee^†, Ji Young Min^†, and Sangwon Byun^*

Sensors, 2021

Code
Separate and joint associations of cardiorespiratory fitness and healthy vascular aging with subclinical atherosclerosis in men

Sae Young Jae^†, Kyung Hyun Lee^†, Hyun Jeong Kim, and 5 more authors

Hypertension, 2022
Age prediction in healthy subjects using RR intervals and heart rate variability: A pilot study based on deep learning

Kyung Hyun Lee^† and Sangwon Byun^*

Applied Sciences, 2023

Code
Novel artificial intelligence-based technology to diagnose asthma using methacholine challenge tests

Noeul Kang^†, Kyung Hyun Lee^†, Sangwon Byun, and 3 more authors

Allergy, Asthma & Immunology Research, 2023

Code
Association between cardiorespiratory fitness and trend of age-related rise in arterial stiffness in individuals with and without hypertension or diabetes

Sae Young Jae^†, Kyung Hyun Lee^†, Hyun Jeong Kim, and 4 more authors

American journal of hypertension, 2025
Validation of an artificial intelligence-based algorithm for predictive performance and risk stratification of sepsis using real-world data from hospitalised patients: a prospective observational study

Ji-Hyun Kim^†, Kyung Hyun Lee^†, Kwang Joon Kim, and 11 more authors

BMJ Health & Care Informatics, 2025
Deep learning-based stress detection from RR intervals in major depressive disorder, panic disorder, and healthy individuals

Kyung Hyun Lee^†, Chul-Hyun Cho^†, Ah Young Kim, and 2 more authors

Frontiers in Psychiatry, 2025

Code