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Reviews in Medical Informatics (97) Digital Health Reviews (1516) Computer-Aided Diagnosis (181) Machine Learning (1730) Methods and Instruments in Medical Informatics (164) New Technologies (95)

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 (36) 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 2024;342(1):47 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 2025;49(3):154 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) 2025;49(3):154 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
  22. Herr K, Lu P, Diamreyan K, Xu H, Mendonca E, Weaver K, Chen J. Estimating prevalence of rare genetic disease diagnoses using electronic health records in a children’s hospital. Human Genetics and Genomics Advances 2024;5(4):100341 View
  23. Matos J, Gallifant J, Chowdhury A, Economou-Zavlanos N, Charpignon M, Gichoya J, Celi L, Nazer L, King H, Wong A. A Clinician’s Guide to Understanding Bias in Critical Clinical Prediction Models. Critical Care Clinics 2024;40(4):827 View
  24. Wang J, Wang G, Wang Y, Wang Y. Development and evaluation of a model for predicting the risk of healthcare-associated infections in patients admitted to intensive care units. Frontiers in Public Health 2024;12 View
  25. Hinrichs N, Roeschl T, Lanmueller P, Balzer F, Eickhoff C, O’Brien B, Falk V, Meyer A, Barage S. Short-term vital parameter forecasting in the intensive care unit: A benchmark study leveraging data from patients after cardiothoracic surgery. PLOS Digital Health 2024;3(9):e0000598 View
  26. Sinha A, Nimbalkar S, Pujara R, Patel P, Shinde M, Sethi S, Aradhya R, Patel D. SimCapture app video performance assessment versus real-time instructor-based performance evaluation of undergraduates in neonatal resuscitation—an agreement study. Journal of Tropical Pediatrics 2024;70(6) View
  27. Düvel J, Lampe D, Kirchner M, Elkenkamp S, Cimiano P, Düsing C, Marchi H, Schmiegel S, Fuchs C, Claßen S, Meier K, Borgstedt R, Rehberg S, Greiner W. An AI-Based Clinical Decision Support System for Antibiotic Therapy in Sepsis (KINBIOTICS): Use Case Analysis. JMIR Human Factors 2025;12:e66699 View
  28. Lim L, Kim M, Cho K, Yoo D, Sim D, Ryu H, Lee H. Multicenter validation of a machine learning model to predict intensive care unit readmission within 48 hours after discharge. eClinicalMedicine 2025;81:103112 View
  29. de Hond A, Vosslamber S, Lange S, Engel F, Lindhout M, Noorlag P, Steyerberg E, Cinà G, Arbous M. When, where, who, what, and why? The five Ws of workflow analysis for implementing an AI decision support tool at the intensive care. Human Factors in Healthcare 2025;7:100095 View
  30. Beaussier M, Guidet B, Aegerter P, Baillard C, Boulkedid R, Desmard M, Pateron D, Nkam L, Misset B. Profiles of the patients admitted to intermediate care units in France: Admission criteria appropriateness and potential outcome benefits (UNISURC project part 2). Journal of Critical Care 2025;88:155078 View
  31. Popoff B, Cabon S, Cuggia M, Bouzillé G, Clavier T. Expectations of Intensive Care Physicians Regarding an AI-Based Decision Support System for Weaning From Continuous Renal Replacement Therapy: Predevelopment Survey Study. JMIR Medical Informatics 2025;13:e63709 View
  32. Yu Z, Fang L, Ding Y. Explainable machine learning model for prediction of 28-day all-cause mortality in immunocompromised patients in the intensive care unit: a retrospective cohort study based on MIMIC-IV database. European Journal of Medical Research 2025;30(1) 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

Conference Proceedings

  1. Orth T, Ambe A, Lovell D, Perrin D. Proceedings of the 2024 Australasian Computer Science Week. Delicate Decisions at the Intersection of Intensive Care and Machine Learning - How Human Information Needs inform the Development of Decision Support View
  2. Sundas A, Singh G, Badotra S, Verma A, Kaur B. 2023 Seventh International Conference on Image Information Processing (ICIIP). Optimizing Length of Stay Prediction After Intubation: An Advanced Machine Learning Model with Real-time Vital Sign Integration View