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

Jul 15, 2026	Our late-breaking abstract, “Reading screening spirometry as a trajectory predicts incident airflow obstruction earlier: a landmark deep-learning analysis of 221,903 adults”, has been accepted as a poster presentation at the European Respiratory Society (ERS) International Congress 2026, to be presented on September 8, 2026.
Jun 04, 2026	Submitted a late-breaking abstract, “Reading screening spirometry as a trajectory predicts incident airflow obstruction earlier: a landmark deep-learning analysis of 221,903 adults”, to the European Respiratory Society (ERS) International Congress 2026 late-breaking abstract session.
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.

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

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

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

Sensors, 2021

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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

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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

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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

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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

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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

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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

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