Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/59520, first published .
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

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

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 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 2025 View