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

Decision Support for Health Professionals (1127) Machine Learning (1370) Preoperative Measures for Prevention (30)

Published on in Vol 7, No 4 (2019): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/14993, first published .
Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study

Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study

Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study

Authors of this article:

Hani Nabeel Mufti1, 2, 3 Author Orcid Image ;   Gregory Marshal Hirsch4 Author Orcid Image ;   Samina Raza Abidi5 Author Orcid Image ;   Syed Sibte Raza Abidi6 Author Orcid Image
Article Authors Cited by (33) Tweetations (7) Metrics

Journals

  1. Coombes C, Coombes K, Fareed N. A novel model to label delirium in an intensive care unit from clinician actions. BMC Medical Informatics and Decision Making 2021;21(1) View
  2. Chua S, Wrigley S, Hair C, Sahathevan R. Prediction of delirium using data mining: A systematic review. Journal of Clinical Neuroscience 2021;91:288 View
  3. Nedadur R, Wang B, Yanagawa B. The cardiac surgeon's guide to artificial intelligence. Current Opinion in Cardiology 2021;36(5):637 View
  4. Son C, Kang W, Lee J, Moon K. Machine Learning to Identify Psychomotor Behaviors of Delirium for Patients in Long-Term Care Facility. IEEE Journal of Biomedical and Health Informatics 2022;26(4):1802 View
  5. Jiang Z, Cai Y, Liu S, Ye P, Yang Y, Lin G, Li S, Xu Y, Zheng Y, Bao Z, Nie S, Gu W. Decreased default mode network functional connectivity with visual processing regions as potential biomarkers for delayed neurocognitive recovery: A resting-state fMRI study and machine-learning analysis. Frontiers in Aging Neuroscience 2023;14 View
  6. von Gerich H, Moen H, Block L, Chu C, DeForest H, Hobensack M, Michalowski M, Mitchell J, Nibber R, Olalia M, Pruinelli L, Ronquillo C, Topaz M, Peltonen L. Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence. International Journal of Nursing Studies 2022;127:104153 View
  7. 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
  8. Hong L, Shen X, Shi Q, Song X, Chen L, Chen W, Chen S, Xue Y, Zhang C, Zhou J. Association Between Hypernatremia and Delirium After Cardiac Surgery: A Nested Case-Control Study. Frontiers in Cardiovascular Medicine 2022;9 View
  9. Kim J, Hua M, Whittington R, Lee J, Liu C, Ta C, Marcantonio E, Goldberg T, Weng C. A machine learning approach to identifying delirium from electronic health records. JAMIA Open 2022;5(2) View
  10. Mulkey M, Albanese T, Kim S, Huang H, Yang B. Delirium detection using GAMMA wave and machine learning: A pilot study. Research in Nursing & Health 2022;45(6):652 View
  11. Jiang Z, Cai Y, Zhang X, Lv Y, Zhang M, Li S, Lin G, Bao Z, Liu S, Gu W. Predicting Delayed Neurocognitive Recovery After Non-cardiac Surgery Using Resting-State Brain Network Patterns Combined With Machine Learning. Frontiers in Aging Neuroscience 2021;13 View
  12. Bishara A, Chiu C, Whitlock E, Douglas V, Lee S, Butte A, Leung J, Donovan A. Postoperative delirium prediction using machine learning models and preoperative electronic health record data. BMC Anesthesiology 2022;22(1) View
  13. Mumtaz H, Saqib M, Ansar F, Zargar D, Hameed M, Hasan M, Muskan P. The future of Cardiothoracic surgery in Artificial intelligence. Annals of Medicine and Surgery 2022;80:104251 View
  14. Lapp L, Bouamrane M, Roper M, Kavanagh K, Schraag S. Definition and Classification of Postoperative Complications After Cardiac Surgery: Pilot Delphi Study. JMIR Perioperative Medicine 2022;5(1):e39907 View
  15. Li Q, Zhao Y, Chen Y, Yue J, Xiong Y. Developing a machine learning model to identify delirium risk in geriatric internal medicine inpatients. European Geriatric Medicine 2022;13(1):173 View
  16. Song Y, Yang X, Luo Y, Ouyang C, Yu Y, Ma Y, Li H, Lou J, Liu Y, Chen Y, Cao J, Mi W. Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study. CNS Neuroscience & Therapeutics 2023;29(1):158 View
  17. (Morikatsu Tsuchiya) 土, (Kazuya Niiyama) 新, (Tadashi Aoyagi) 青, (Kenta Hodoshima) 程, (Seiichi Takahashi) 髙, (Takashi Mato) 間. せん妄の予測に対する機械学習の利用状況：スコーピングレビュー(The use of machine learning for the prediction of delirium: a scoping review). Nihon Kyukyu Igakukai Zasshi: Journal of Japanese Association for Acute Medicine 2022;33(3):95 View
  18. 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
  19. Hosseinzadeh Kasani P, Lee J, Park C, Yun C, Jang J, Lee S. Evaluation of nutritional status and clinical depression classification using an explainable machine learning method. Frontiers in Nutrition 2023;10 View
  20. Matsumoto K, Nohara Y, Sakaguchi M, Takayama Y, Fukushige S, Soejima H, Nakashima N, Kamouchi M. Temporal Generalizability of Machine Learning Models for Predicting Postoperative Delirium Using Electronic Health Record Data: Model Development and Validation Study. JMIR Perioperative Medicine 2023;6:e50895 View
  21. Shahidi F, Rennert-May E, D’Souza A, Crocker A, Faris P, Leal J. Machine learning risk estimation and prediction of death in continuing care facilities using administrative data. Scientific Reports 2023;13(1) View
  22. Tian Y, Ji B, Diao X, Wang C, Wang W, Gao Y, Wang S, Zhou C, Zhang Q, Gao S, Xu X, Liu J, Wang J, Wang Y. Dynamic predictive scores for cardiac surgery-associated agitated delirium: a single-center retrospective observational study. Journal of Cardiothoracic Surgery 2023;18(1) View
  23. Cho E, Kim S, Heo S, Shin J, Hwang S, Kwon E, Lee S, Kim S, Kang B. Machine learning-based predictive models for the occurrence of behavioral and psychological symptoms of dementia: model development and validation. Scientific Reports 2023;13(1) View
  24. Zhao X, Li J, Xie X, Fang Z, Feng Y, Zhong Y, Chen C, Huang K, Ge C, Shi H, Si Y, Zou J. Online interpretable dynamic prediction models for postoperative delirium after cardiac surgery under cardiopulmonary bypass developed based on machine learning algorithms: A retrospective cohort study. Journal of Psychosomatic Research 2024;176:111553 View
  25. Nagata C, Hata M, Miyazaki Y, Masuda H, Wada T, Kimura T, Fujii M, Sakurai Y, Matsubara Y, Yoshida K, Miyagawa S, Ikeda M, Ueno T. Development of postoperative delirium prediction models in patients undergoing cardiovascular surgery using machine learning algorithms. Scientific Reports 2023;13(1) View
  26. Yang T, Yang H, Liu Y, Liu X, Ding Y, Li R, Mao A, Huang Y, Li X, Zhang Y, Yu F. Postoperative delirium prediction after cardiac surgery using machine learning models. Computers in Biology and Medicine 2024;169:107818 View
  27. 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
  28. Miyazawa Y, Katsuta N, Nara T, Nojiri S, Naito T, Hiki M, Ichikawa M, Takeshita Y, Kato T, Okumura M, Tobita M, Jakovljevic M. Identification of risk factors for the onset of delirium associated with COVID-19 by mining nursing records. PLOS ONE 2024;19(1):e0296760 View
  29. Weidmann A, Watson E. Novel opportunities for clinical pharmacy research: development of a machine learning model to identify medication related causes of delirium in different patient groups. International Journal of Clinical Pharmacy 2024 View
  30. Sheehan K, Shin S, Hall E, Mak D, Lapointe-Shaw L, Tang T, Marwaha S, Gandell D, Rawal S, Inouye S, Verma A, Razak F, Righi L. Characterizing medical patients with delirium: A cohort study comparing ICD-10 codes and a validated chart review method. PLOS ONE 2024;19(5):e0302888 View
  31. Vaidya Y, Shumway S. Artificial intelligence: The future of cardiothoracic surgery. The Journal of Thoracic and Cardiovascular Surgery 2024 View

Books/Policy Documents

  1. Ebnali M, Zenati M, Dias R. Artificial Intelligence in Clinical Practice. View