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

Reviews in Medical Informatics (82) Digital Health Reviews (1174) Computer-Aided Diagnosis (94) Machine Learning (1370) Methods and Instruments in Medical Informatics (101) New Technologies (64)

Published on in Vol 10, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28781, first published .
State of the Art of Machine Learning–Enabled Clinical Decision Support in Intensive Care Units: Literature Review

State of the Art of Machine Learning–Enabled Clinical Decision Support in Intensive Care Units: Literature Review

State of the Art of Machine Learning–Enabled Clinical Decision Support in Intensive Care Units: Literature Review

Authors of this article:

Na Hong1 Author Orcid Image ;   Chun Liu1 Author Orcid Image ;   Jianwei Gao1 Author Orcid Image ;   Lin Han1 Author Orcid Image ;   Fengxiang Chang1 Author Orcid Image ;   Mengchun Gong1 Author Orcid Image ;   Longxiang Su2 Author Orcid Image
Article Authors Cited by (25) Tweetations (11) Metrics

Journals

  1. Coombes C, Coombes K, Fareed N. Sequences of Events from the Electronic Medical Record and the Onset of Infection. Chemistry & Biodiversity 2022;19(11) View
  2. Merkelbach K, Schaper S, Diedrich C, Fritsch S, Schuppert A. Novel architecture for gated recurrent unit autoencoder trained on time series from electronic health records enables detection of ICU patient subgroups. Scientific Reports 2023;13(1) View
  3. Pollack M, Trujillo Rivera E, Morizono H, Patel A. Clinical Instability Is a Sign of Severity of Illness: A Cohort Study. Pediatric Critical Care Medicine 2023;24(9):e425 View
  4. Valiente Fernández M, García Fuentes C, Delgado Moya F, Marcos Morales A, Fernández Hervás H, Barea Mendoza J, Mudarra Reche C, Bermejo Aznárez S, Muñoz Calahorro R, López García L, Monforte Escobar F, Chico Fernández M. Could machine learning algorithms help us predict massive bleeding at prehospital level?. Medicina Intensiva (English Edition) 2023;47(12):681 View
  5. Chen Y, Chen H, Sun Q, Zhai R, Liu X, Zhou J, Li S. Machine learning model identification and prediction of patients’ need for ICU admission: A systematic review. The American Journal of Emergency Medicine 2023;73:166 View
  6. Singer P, Robinson E, Raphaeli O. Gastrointestinal failure, big data and intensive care. Current Opinion in Clinical Nutrition & Metabolic Care 2023;26(5):476 View
  7. Hassanzadeh H, Joshi S, Taghavi S. Predicting buoyant jet characteristics: a machine learning approach. Chemical Product and Process Modeling 2024;19(2):163 View
  8. Li Y, Liu Y, Wang M, Huang Y. Prediction of gestational diabetes mellitus at the first trimester: machine-learning algorithms. Archives of Gynecology and Obstetrics 2023;309(6):2557 View
  9. Valiente Fernández M, García Fuentes C, Delgado Moya F, Marcos Morales A, Fernández Hervás H, Barea Mendoza J, Mudarra Reche C, Bermejo Aznárez S, Muñoz Calahorro R, López García L, Monforte Escobar F, Chico Fernández M. ¿Podrían ayudarnos los algoritmos de machine learning en la predicción de hemorragia masiva a nivel prehospitalario?. Medicina Intensiva 2023;47(12):681 View
  10. Pereira J, Antunes N, Rosa J, Ferreira J, Mogo S, Pereira M. Intelligent Clinical Decision Support System for Managing COPD Patients. Journal of Personalized Medicine 2023;13(9):1359 View
  11. Bieliński A, Rojek I, Mikołajewski D. Comparison of Selected Machine Learning Algorithms in the Analysis of Mental Health Indicators. Electronics 2023;12(21):4407 View
  12. Jenkinson A, Dassios T, Greenough A. Artificial intelligence in the NICU to predict extubation success in prematurely born infants. Journal of Perinatal Medicine 2024;52(2):119 View
  13. Agrawal N, Rabiee M, Jabbari M. Contextual relationships in Juran’s quality principles for business sustainable growth under circular economy perspective: a decision support system approach. Annals of Operations Research 2023 View
  14. Molfino N, Turcatel G, Riskin D. Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review. Advances in Therapy 2024;41(2):534 View
  15. Baddal B, Taner F, Uzun Ozsahin D. Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review. Diagnostics 2024;14(5):484 View
  16. Henry K, Giannini H. Early Warning Systems for Critical Illness Outside the Intensive Care Unit. Critical Care Clinics 2024;40(3):561 View
  17. Barea Mendoza J, Valiente Fernandez M, Pardo Fernandez A, Gómez Álvarez J. Perspectivas actuales sobre el uso de la inteligencia artificial en la seguridad del paciente crítico. Medicina Intensiva 2024 View
  18. Barea Mendoza J, Valiente Fernandez M, Pardo Fernandez A, Gómez Álvarez J. Current perspectives on the use of artificial intelligence in critical patient safety. Medicina Intensiva (English Edition) 2024 View
  19. Romanelli A, Palmese S, De Vita S, Calicchio A, Gammaldi R. Stratifying Mortality Risk in Intensive Care: A Comprehensive Analysis Using Cluster Analysis and Classification and Regression Tree Algorithms. Intensive Care Research 2024;4(2):116 View
  20. Xie H, Wang B, Hong Y. A deep learning approach for acute liver failure prediction with combined fully connected and convolutional neural networks. Technology and Health Care 2024;32:555 View
  21. Nguyen Q, Tran M, Prabhakaran V, Liu A, Nguyen G. Compact machine learning model for the accurate prediction of first 24-hour survival of mechanically ventilated patients. Frontiers in Medicine 2024;11 View

Books/Policy Documents

  1. Khadela A, Popat S, Ajabiya J, Valu D, Savale S, Chavda V. Bioinformatics Tools for Pharmaceutical Drug Product Development. View
  2. Georgoutsos A, Kerasiotis P, Kantere V. Web Information Systems Engineering – WISE 2023. View