Accessibility settings

This paper is in the following e-collection/theme issue:

Artificial Intelligence (4758) Clinical Information and Decision Making (3698) Decision Support for Health Professionals (2228) Dementia and Cognitive Decline (878) Machine Learning (3167) Intensive Care Unit (ICU) (190)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/59520, first published .
Child's hand holding an adult's hand with an IV drip in a hospital bed

Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study

Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study

Authors of this article:

Chanmin Park1 Author Orcid Image ;   Changho Han1 Author Orcid Image ;   Su Kyeong Jang2 Author Orcid Image ;   Hyungjun Kim2 Author Orcid Image ;   Sora Kim3 Author Orcid Image ;   Byung Hee Kang4 Author Orcid Image ;   Kyoungwon Jung4 Author Orcid Image ;   Dukyong Yoon1 Author Orcid Image
Article Authors Cited by (18) Tweetations Metrics

Journals

  1. Ying C, Xiaona L, Aili Z, Zengxiang W, Ying W, Yu P, Hongbo Z, Danni W, Meiping J, Hongyuan D. Development and validation of a nomogram model for predicting postoperative delirium in elderly patients with oral cancer: a retrospective study. BMC Oral Health 2025;25(1) View
  2. Al-Taie S, Fedwi M, Merza M, Alshahrani M, Rekha M, Kundlas M, Janney J, Sahoo S, Ridha-Salman H, Khosravi M. Evaluation of different sedation scales in the ICU management of COVID-19 patients. Scientific Reports 2025;15(1) View
  3. Wu C, Chang Y, Tranyor V, Shen Hsiao S, Guo S, Lin S, Hou S, Chiu H. Machine learning-based prediction of delirium in older patients with chronic kidney disease requiring intensive care: A hospital-based retrospective cohort study. Journal of Psychosomatic Research 2026;200:112454 View
  4. Bi A, Li T, Cheng G, Hu J. Artificial intelligence applications in intensive care unit nursing: A narrative review (2020–2025). DIGITAL HEALTH 2025;11 View
  5. Chen W, Ding L, Sha Y, lu g, Qian K, Wang B, Wang H. Risk prediction models for delirium in ICU patients: a systematic review and critical appraisal. BMC Anesthesiology 2025;26(1) View
  6. Qin C, Zeng L, Zhang J, Zhang J, Tao M, Zhou J. Artificial Intelligence‐Based Delirium Prediction Model for Post‐Cardiac Surgery Patients: A Scoping Review. Journal of Advanced Nursing 2026;82(8):7735 View
  7. Bulut A, Bahadır Yılmaz E. The impact of window on delirium, sedation, and sleep quality of intensive care patients: a prospective study. Turkish Journal of Intensive Care 2025;23(4):268 View
  8. Pandian V, Rahimibashar F, Arabfard M, Alhalaiqa F, Vahedian-Azimi A. The role of AI-driven communication in delirium prevention, detection, and care for critically ill ICU patients: A systematic review with inductive thematic synthesis. Intensive and Critical Care Nursing 2026;93:104323 View
  9. Nawan A, Wang G. EEG‐Derived Index Predicts Postoperative Delirium in Elderly Patients With Hip Fracture: A Prospective Study From a Tertiary Medical Center. Brain and Behavior 2026;16(1) View
  10. Garcés M, Montoya J, Martínez M, García J, Rincón E. Deep Learning and Noninvasive Sensors for Detecting Physiological Dysregulation: A Scoping Review. Journal of Medical Systems 2026;50(1) View
  11. Aytolgn H, Mersha G, Endalew N, Tegegne S, Agegnehu A, Khatra H. Assessment of Knowledge, Attitude, Practice and Associated Factors Towards Sedation Among Nurses Working in Intensive Care Units of Comprehensive Specialized Hospitals: A Multicentre Cross‐Sectional Study. Nursing Research and Practice 2026;2026(1) View
  12. Zuo D, Wang Y. Nomogram and machine learning models for predicting the risk of delirium in ICU patients with NSTEMI. Medicine 2026;105(21):e49062 View
  13. Yang J, Yang S, Shao Z, Chen T, Shen H, Zhou P, Xia B, Lei X, Wang L, Xue D, Zheng S, Yu Y, Zhang Z. Large language models in emergency and critical care medicine: a comprehensive review of applications, challenges, and future directions. Burns & Trauma 2026;14 View
  14. Grigorescu B, Azamfirei L, Bora S, Bica D, Săplăcan I, Gergo R, Veres M. Physiological Data Integration and Predictive Modeling in Intensive Care. Life 2026;16(8):1254 View
  15. Huang W, Tong S, Gao L. Development and external validation of an online interpretable machine-learning model for predicting delirium risk in acute heart failure. Journal of International Medical Research 2026;54(8) View
  16. Lee G, Won J, Cho E, Kim J, Chung K, Kim K, Yun S, Lee H, Kim J. Value of AI in Critical Care Using Real-World Evidence on Intensive Care Unit Mortality Prediction: Cost-Utility Analysis. Journal of Medical Internet Research 2026;28:e93466 View
  17. Fu K, Song B, Luo Y, Liu C. Machine learning–based prediction of incident delirium after a 24-hour landmark in critically ill patients with acute pancreatitis: Model development and external validation using the MIMIC-IV and eICU databases. Science Progress 2026;109(3) View

Conference Proceedings

  1. Shrivastav S, Kaur G, Singh K, Singh N, Kumar Y. 2026 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). AIDS Infection Prediction from Clinical and Demographic Data Using Ensemble and Linear Models View