{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:35:37Z","timestamp":1785404137411,"version":"3.56.0"},"reference-count":27,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T00:00:00Z","timestamp":1778284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","award":["RS-2021-KH114109"],"award-info":[{"award-number":["RS-2021-KH114109"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]},{"award":["RS-2021-KH114109"],"award-info":[{"award-number":["RS-2021-KH114109"]}],"id":[{"id":"https:\/\/ror.org\/00fdzyk40","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background\/Objectives: In critically ill patients, endotracheal intubation (EI) is often performed to secure the airway or mechanical ventilation. Accurately predicting the timing of intubation significantly affects patient outcomes. We developed an artificial intelligence (AI) model designed for real-time risk stratification of patients requiring EI. Methods: We utilized the Medical Information Mart for Intensive Care-IV (MIMIC-IV) 2.2 dataset and performed model development using 15 clinical variables, including vital signs, Glasgow Coma Scale (GCS) score, and arterial blood gas analysis results. Patients intubated before or within 1 h of intensive care unit (ICU) admission were excluded. Clinical data from the ICU inherently consists of continuous time-series measurements. Traditional machine learning models often treat this information as static tabular data, neglecting vital temporal dynamics and patient history. Conversely, deep learning time-series approaches can capture these complex patterns over time. Thus, we applied the Gated Recurrent Unit with Decay++ (GRU-D++) model to predict the need for EI. GRU-D++ is an extension of the GRU and GRU-D. It builds upon the GRU-D to provide improved performance when handling datasets with exceptionally high rates of missing values. GRU-D++ is a time series deep learning model with an automatic mechanism for imputing missing values. This built-in capability eliminates the need for additional data preprocessing and has previously demonstrated high predictive performance. Using the 15 variables, we evaluated the optimal timing for EI in ICU-admitted patients by applying various AI models. Results: Among these, the GRU-D++ model demonstrated AUROC of 0.888, AUPR of 0.481, sensitivity of 0.474, specificity of 0.995, precision of 0.511, and F1 score of 0.491 on MIMIC-IV dataset. For KNUH dataset, the model demonstrated AUROC of 0.913, AUPR of 0.063, sensitivity of 0.162, specificity of 0.997, precision of 0.137, and F1 score of 0.147 within the 2 h in advance scenario. Furthermore, when compared with conventional scoring systems such as the Heart rate, Acidosis, Consciousness, Oxygenation, Respiratory rate (HACOR) score and Respiratory rate-Oxygenation (ROX) index, the GRU-D++ model also showed better performance predictive accuracy. Conclusions: The AI-based intubation prediction model developed in this study holds potential as a real-time risk stratification tool, providing timely risk assessments regarding the need EI. While operational threshold recalibration is essential prior to clinical deployment, further prospective multicenter studies are required to validate the clinical utility of this model in real-time practice.<\/jats:p>","DOI":"10.3390\/jcm15103642","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T17:17:42Z","timestamp":1778519862000},"page":"3642","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of an Artificial Intelligence Model to Predict Endotracheal Intubation in Critically Ill Patients in Real Time"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5602-9572","authenticated-orcid":false,"given":"Da Hye","family":"Moon","sequence":"first","affiliation":[{"name":"Department of Internal Medicine, Kangwon National University, Chuncheon 24341, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1122-5363","authenticated-orcid":false,"given":"Minkyu","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Research & Development, Ziovision Co., Ltd., Chuncheon 24341, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1320-4163","authenticated-orcid":false,"given":"Seon-Sook","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Kangwon National University, Chuncheon 24341, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6855-8107","authenticated-orcid":false,"given":"Tae-Hoon","family":"Kim","sequence":"additional","affiliation":[{"name":"Institute of Medical Science, School of Medicine, Kangwon National University, Chuncheon 24341, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2626-0510","authenticated-orcid":false,"given":"Dohyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Research & Development, Ziovision Co., Ltd., Chuncheon 24341, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2927-370X","authenticated-orcid":false,"given":"Woo Jin","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Kangwon National University, Chuncheon 24341, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4597-0281","authenticated-orcid":false,"given":"Seung-Joon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Kangwon National University, Chuncheon 24341, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Research & Development, Ziovision Co., Ltd., Chuncheon 24341, Republic of Korea"},{"name":"Department of Computer Science and Engineering, Kangwon National University, Chuncheon 24341, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeongwon","family":"Heo","sequence":"additional","affiliation":[{"name":"Department of Pulmonology, Chinjujeil Hospital, Jinju 52709, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3594-8948","authenticated-orcid":false,"given":"Hyun-Soo","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Research & Development, Ziovision Co., Ltd., Chuncheon 24341, Republic of Korea"},{"name":"Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8151-7970","authenticated-orcid":false,"given":"Yeonjeong","family":"Heo","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Kangwon National University, Chuncheon 24341, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1097\/00000542-199502000-00007","article-title":"Death and other complications of emergency airway management in critically ill adults. A prospective investigation of 297 tracheal intubations","volume":"82","author":"Schwartz","year":"1995","journal-title":"Anesthesiology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1097\/00003246-198411000-00012","article-title":"A simplified acute physiology score for ICU patients","volume":"12","author":"Loirat","year":"1984","journal-title":"Crit. Care Med."},{"key":"ref_3","first-page":"46","article-title":"Validating the use of the Apache II score in a tertiary South African ICU","volume":"21","author":"Kidd","year":"2005","journal-title":"S. Afr. J. Crit. Care"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1378\/chest.100.6.1619","article-title":"The Apache III prognostic system: Risk prediction of hospital mortality for critically III hospitalized adults","volume":"100","author":"Knaus","year":"1991","journal-title":"Chest"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1097\/01.CCM.0000215112.84523.F0","article-title":"Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today\u2019s critically ill patients","volume":"34","author":"Zimmerman","year":"2006","journal-title":"Crit. Care Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1001\/jama.2016.0287","article-title":"The third international consensus definitions for sepsis and septic shock (Sepsis-3)","volume":"315","author":"Singer","year":"2016","journal-title":"JAMA"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1007\/s001340050931","article-title":"The use of maximum SOFA score to quantify organ dysfunction\/failure in intensive care. Results of a prospective, multicentre study. Working Group on Sepsis related Problems of the ESICM","volume":"25","author":"Moreno","year":"1999","journal-title":"Intensive Care Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/S0140-6736(74)91639-0","article-title":"Assessment of coma and impaired consciousness: A practical scale","volume":"2","author":"Teasdale","year":"1974","journal-title":"Lancet"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1016\/S1474-4422(14)70120-6","article-title":"The Glasgow Coma Scale at 40 years: Standing the test of time","volume":"13","author":"Teasdale","year":"2014","journal-title":"Lancet Neurol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1186\/cc8204","article-title":"Clinical review: Scoring systems in the critically ill","volume":"14","author":"Vincent","year":"2010","journal-title":"Crit. Care"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1007\/s00134-016-4601-3","article-title":"Assessment of heart rate, acidosis, consciousness, oxygenation, and respiratory rate to predict noninvasive ventilation failure in hypoxemic patients","volume":"43","author":"Duan","year":"2017","journal-title":"Intensive Care Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.jcrc.2016.05.022","article-title":"Predicting success of high-flow nasal cannula in pneumonia patients with hypoxemic respiratory failure: The utility of the ROX index","volume":"35","author":"Roca","year":"2016","journal-title":"J. Crit. Care"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.compind.2018.01.017","article-title":"Mortality prediction based on imbalanced high-dimensional ICU big data","volume":"98","author":"Liu","year":"2018","journal-title":"Comput. Ind."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1186\/s13054-019-2564-9","article-title":"Use of machine learning to analyse routinely collected intensive care unit data: A systematic review","volume":"23","author":"Shillan","year":"2019","journal-title":"Crit. Care"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"20931","DOI":"10.1038\/s41598-020-77893-3","article-title":"Predicting the need for intubation in the first 24 h after critical care admission using machine learning approaches","volume":"10","author":"Siu","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Zhang, J., Wang, G., Yao, R., Ren, C., Chen, G., Jin, X., Guo, J., Liu, S., and Zheng, H. (2021). Machine learning prediction models for mechanically ventilated patients: Analyses of the MIMIC-III database. Front. Med., 8.","DOI":"10.3389\/fmed.2021.662340"},{"key":"ref_17","first-page":"31","article-title":"Predictors of prolonged mechanical ventilation in patients admitted to intensive care units: A systematic review","volume":"13","author":"Ghauri","year":"2019","journal-title":"Int. J. Health Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kim, Y., Kim, H., Choi, J., Cho, K., Yoo, D., Lee, Y., Park, S.J., Jeong, M.H., Jeong, S.H., and Park, K.H. (2023). Early prediction of need for invasive mechanical ventilation in the neonatal intensive care unit using artificial intelligence and electronic health records: A clinical study. BMC Pediatr., 23.","DOI":"10.1186\/s12887-023-04350-1"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kim, M., Kim, T.H., Kim, D., Lee, D., Kim, D., Heo, J., Kang, S., Ha, T., Kim, J., and Moon, D.H. (2023). In-advance prediction of pressure ulcers via deep-learning-based robust missing value imputation on real-time intensive care variables. J. Clin. Med., 13.","DOI":"10.3390\/jcm13010036"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-022-01899-x","article-title":"MIMIC-IV, a freely accessible electronic health record dataset","volume":"10","author":"Johnson","year":"2023","journal-title":"Sci. Data"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6085","DOI":"10.1038\/s41598-018-24271-9","article-title":"Recurrent neural networks for multivariate time series with missing values","volume":"8","author":"Che","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_22","first-page":"4768","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1186\/s13054-022-04060-7","article-title":"An updated HACOR score for predicting the failure of noninvasive ventilation: A multicenter prospective observational study","volume":"26","author":"Duan","year":"2022","journal-title":"Crit. Care"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1164\/rccm.201803-0589OC","article-title":"An index combining respiratory rate and oxygenation to predict outcome of nasal high-flow therapy","volume":"199","author":"Roca","year":"2019","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1007\/s00521-024-10468-9","article-title":"Multitask multilayer-prediction model for predicting mechanical ventilation and the associated mortality rate","volume":"37","author":"Tarek","year":"2025","journal-title":"Neural Comput. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1186\/s13613-019-0585-9","article-title":"Early prediction of noninvasive ventilation failure in COPD patients: Derivation, internal validation, and external validation of a simple risk score","volume":"9","author":"Duan","year":"2019","journal-title":"Ann. Intensive Care"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Stoian, M., Azamfirei, L., St\u00eengaciu, A.C., Negulici, L.-M., V\u0103sie\u0219iu, A.M., Manea, A., and Stoian, A. (2025). Early Diagnostic Markers and Risk Stratification in Sepsis: Prognostic Value of Neutrophil-to-Lymphocyte Ratio, Platelets, and the Carmeli Score. Biomedicines, 13.","DOI":"10.3390\/biomedicines13112658"}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/10\/3642\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T10:00:48Z","timestamp":1779357648000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/10\/3642"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,9]]},"references-count":27,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["jcm15103642"],"URL":"https:\/\/doi.org\/10.3390\/jcm15103642","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,9]]}}}