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Seheon Choi

ORCID: 0009-0000-0506-6971
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About
Contact & Profiles
A5103271059
Research Areas
  • Sepsis Diagnosis and Treatment
  • Hydrological Forecasting Using AI
  • Machine Learning in Healthcare
 Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study
OPENALEX - Publications 
Sang Won Park Na Young Yeo Seonguk Kang Tae-Jun Ha T. Y. Kim and 18 more Doohee Lee Dowon Kim Seheon Choi Minkyu Kim Dong-Hoon Lee DoHyeon Kim Woo Jin Kim Seung‐Joon Lee Yeonjeong Heo Da Hye Moon Seon‐Sook Han Yoon Kim Hyun-Soo Choi Dong Kyu Oh Su Yeon Lee Mi-Hyeon Park Chae‐Man Lim Jeongwon Heo

Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates patients with can be predicted early, medical resources allocated efficiently. We constructed machine learning (ML) models to predict a hospital emergency department.

10.3346/jkms.2024.39.e53 article EN cc-by-nc Journal of Korean Medical Science 2024-01-01
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