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

Machine Learning (1465) Clinical Information and Decision Making (1407) Decision Support for Health Professionals (1185) Artificial Intelligence (1371) Intensive Care Unit (ICU) (112)

Published on in Vol 9, No 7 (2021): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23401, first published .
A Machine Learning–Based Algorithm for the Prediction of Intensive Care Unit Delirium (PRIDE): Retrospective Study

A Machine Learning–Based Algorithm for the Prediction of Intensive Care Unit Delirium (PRIDE): Retrospective Study

A Machine Learning–Based Algorithm for the Prediction of Intensive Care Unit Delirium (PRIDE): Retrospective Study

Authors of this article:

Sujeong Hur1, 2 Author Orcid Image ;   Ryoung-Eun Ko3 Author Orcid Image ;   Junsang Yoo4 Author Orcid Image ;   Juhyung Ha5 Author Orcid Image ;   Won Chul Cha2, 6, 7 Author Orcid Image ;   Chi Ryang Chung3 Author Orcid Image
Article Authors Cited by (13) Tweetations (4) Metrics

Journals

  1. Bishara A, Maze E, Maze M. Considerations for the implementation of machine learning into acute care settings. British Medical Bulletin 2022;141(1):15 View
  2. Maviglia R, Michi T, Passaro D, Raggi V, Bocci M, Piervincenzi E, Mercurio G, Lucente M, Murri R. Machine Learning and Antibiotic Management. Antibiotics 2022;11(3):304 View
  3. Yoon H, Yang H, Jung C, Lee H. Artificial intelligence in perioperative medicine: a narrative review. Korean Journal of Anesthesiology 2022;75(3):202 View
  4. Xie Q, Wang X, Pei J, Wu Y, Guo Q, Su Y, Yan H, Nan R, Chen H, Dou X. Machine Learning–Based Prediction Models for Delirium: A Systematic Review and Meta-Analysis. Journal of the American Medical Directors Association 2022;23(10):1655 View
  5. Mueller B, Street W, Carnahan R, Lee S. Evaluating the performance of machine learning methods for risk estimation of delirium in patients hospitalized from the emergency department. Acta Psychiatrica Scandinavica 2023;147(5):493 View
  6. Wang M, Kuo Y, Kuo L, Liang H, Cheng Y, Yeh Y, Tsai M, Chan W, Chiu C, Chao A, Chou N, Yeh Y, Ku S. Early prediction of delirium upon intensive care unit admission: Model development, validation, and deployment. Journal of Clinical Anesthesia 2023;88:111121 View
  7. Zhang Y, Hu J, Hua T, Zhang J, Zhang Z, Yang M. Development of a machine learning-based prediction model for sepsis-associated delirium in the intensive care unit. Scientific Reports 2023;13(1) View
  8. Lei L, Zhang S, Yang L, Yang C, Liu Z, Xu H, Su S, Wan X, Xu M. Machine learning-based prediction of delirium 24 h after pediatric intensive care unit admission in critically ill children: A prospective cohort study. International Journal of Nursing Studies 2023;146:104565 View
  9. Strating T, Shafiee Hanjani L, Tornvall I, Hubbard R, Scott I. Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models. BMJ Health & Care Informatics 2023;30(1):e100767 View
  10. Wang J, Ji M, Han Y, Wu Y. Development and Usability Testing of a Mobile App–Based Clinical Decision Support System for Delirium: Randomized Crossover Trial. JMIR Aging 2024;7:e51264 View
  11. Kim Y, Lee H, Woo H, Lee S, Hong M, Jung E, Yoo S, Lee J, Yon D, Kang B. Machine learning-based model to predict delirium in patients with advanced cancer treated with palliative care: a multicenter, patient-based registry cohort. Scientific Reports 2024;14(1) View