@inproceedings{cbbdb54aacc440d1b8f6ad609af376d7,
title = "Predictive AI Model for Endotracheal Intubation Timing in Critically Ill Patients",
abstract = "Endotracheal intubation is used in critically ill patients to provide oxygen and apply mechanical ventilation, and identifying the exact timing of intubation plays a crucial role in the patient's prognosis. We developed an artificial intelligence-based model to predict the timing of intubation. We found that the predictive performance of our developed GRU-D++ model was the best among various models.",
keywords = "Artificial intelligence, Critically ill patient, Endotracheal intubation",
author = "Yeonjeong Heo and Minkyu Kim and Kim, \{Tae Hoon\} and Dohyun Kim and Moon, \{Da Hye\} and Choi, \{Hyun Soo\} and Han, \{Seon Sook\}",
note = "Publisher Copyright: {\textcopyright} 2025 The Authors.; 20th World Congress on Medical and Health Informatics, MEDINFO 2025 ; Conference date: 09-08-2025 Through 13-08-2025",
year = "2025",
month = aug,
day = "7",
doi = "10.3233/SHTI251242",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "1842--1843",
editor = "Househ, \{Mowafa S.\} and Househ, \{Mowafa S.\} and Tariq, \{Zain Ul Abideen\} and Mahmood Al-Zubaidi and Uzair Shah and Elaine Huesing",
booktitle = "MEDINFO 2025 - Healthcare Smart x Medicine Deep",
}