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

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

Published on in Vol 9, No 5 (2021): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21347, first published .
Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation

Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation

Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation

Authors of this article:

Khalid Alghatani1 Author Orcid Image ;   Nariman Ammar2 Author Orcid Image ;   Abdelmounaam Rezgui3 Author Orcid Image ;   Arash Shaban-Nejad2 Author Orcid Image
Article Authors Cited by (42) Tweetations (3) Metrics

Journals

  1. Wang P, Sun X, Qu Z. Analysis of an Economic Coupling Relationship Model of the Coastal Ecological Fragile Zone Based on a Machine Learning Model. Wireless Communications and Mobile Computing 2022;2022:1 View
  2. Tseng T, Su C, Lai F. Fast Healthcare Interoperability Resources for Inpatient Deterioration Detection With Time-Series Vital Signs: Design and Implementation Study. JMIR Medical Informatics 2022;10(10):e42429 View
  3. Ding X, Wen J, Yue X, Zhao Y, Qi C, Wang D, Wei X. Effect of comprehensive nursing intervention for congenital heart disease in children: A meta-analysis. Medicine 2022;101(41):e31184 View
  4. Fan G, Yang S, Liu H, Xu N, Chen Y, He J, Su X, Pang M, Liu B, Han L, Rong L. Machine Learning-based Prediction of Prolonged Intensive Care Unit Stay for Critical Patients with Spinal Cord Injury. Spine 2022;47(9):E390 View
  5. Alghatani K, Ammar N, Rezgui A, Shaban-Nejad A. Precision Clinical Medicine Through Machine Learning: Using High and Low Quantile Ranges of Vital Signs for Risk Stratification of ICU Patients. IEEE Access 2022;10:52418 View
  6. Chiu C, Wu C, Chien T, Kao L, Li C, Chu C. Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method. International Journal of Environmental Research and Public Health 2023;20(5):4340 View
  7. González-Nóvoa J, Busto L, Campanioni S, Fariña J, Rodríguez-Andina J, Vila D, Veiga C. Two-Step Approach for Occupancy Estimation in Intensive Care Units Based on Bayesian Optimization Techniques. Sensors 2023;23(3):1162 View
  8. Kim D, Jin B. Development and Comparative Performance of Physiologic Monitoring Strategies in the Emergency Department. JAMA Network Open 2022;5(9):e2233712 View
  9. Wang S, Li J, Wang Q, Jiao Z, Yan J, Liu Y, Yu R. A data-driven medical knowledge discovery framework to predict the length of ICU stay for patients undergoing craniotomy based on electronic medical records. Mathematical Biosciences and Engineering 2022;20(1):837 View
  10. Jana S, Dasgupta T, Dey L. Predicting medical events and ICU requirements using a multimodal multiobjective transformer network. Experimental Biology and Medicine 2022;247(22):1988 View
  11. Rogerson C, Heneghan J, Kohne J, Goodman D, Slain K, Cecil C, Kane J, Hall M. Machine learning models to predict and benchmark PICU length of stay with application to children with critical bronchiolitis. Pediatric Pulmonology 2023;58(6):1777 View
  12. Atallah L, Nabian M, Brochini L, Amelung P. Machine Learning for Benchmarking Critical Care Outcomes. Healthcare Informatics Research 2023;29(4):301 View
  13. Gaïffas S, Merad I, Yu Y. WildWood: A New Random Forest Algorithm. IEEE Transactions on Information Theory 2023;69(10):6586 View
  14. Alam M. An efficient random forest algorithm-based telemonitoring framework to predict mortality and length of stay of patients in ICU. Multimedia Tools and Applications 2023;83(17):50581 View
  15. Zhao S, Tang G, Liu P, Wang Q, Li G, Ding Z. Improving Mortality Risk Prediction with Routine Clinical Data: A Practical Machine Learning Model Based on eICU Patients. International Journal of General Medicine 2023;Volume 16:3151 View
  16. Xian C, de Souza C, Rodrigues F. Health outcome predictive modelling in intensive care units. Operations Research for Health Care 2023;39:100409 View
  17. Hempel L, Sadeghi S, Kirsten T. Prediction of Intensive Care Unit Length of Stay in the MIMIC-IV Dataset. Applied Sciences 2023;13(12):6930 View
  18. Bhadouria A, Singh R. Machine learning model for healthcare investments predicting the length of stay in a hospital & mortality rate. Multimedia Tools and Applications 2023;83(9):27121 View
  19. Cuadrado D, Valls A, Riaño D. Predicting Intensive Care Unit Patients’ Discharge Date with a Hybrid Machine Learning Model That Combines Length of Stay and Days to Discharge. Mathematics 2023;11(23):4773 View
  20. Zhang M, Kuo T. Early prediction of long hospital stay for Intensive Care units readmission patients using medication information. Computers in Biology and Medicine 2024;174:108451 View
  21. Ke Y, Yang R, Liu N. Comparing Open-Access Database and Traditional Intensive Care Studies Using Machine Learning: Bibliometric Analysis Study. Journal of Medical Internet Research 2024;26:e48330 View
  22. 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
  23. Kuan L, Chin L, De L, Cheng C, Tuao Z, Zixian Y, Roy D, Roy D. Predictive Analysis of Patient Risk of Death in ICU: A Bibliometric Analysis. SHS Web of Conferences 2024;194:01005 View
  24. Alsinglawi B, Alnajjar F, Alorjani M, Al-Shari O, Munoz M, Mubin O. Predicting Hospital Stay Length Using Explainable Machine Learning. IEEE Access 2024;12:90571 View
  25. Shaik T, Tao X, Xie H, Li L, Yong J, Li Y. Graph-Enabled Reinforcement Learning for Time Series Forecasting With Adaptive Intelligence. IEEE Transactions on Emerging Topics in Computational Intelligence 2024;8(4):2908 View
  26. Shah R, Almeida M, Liu C, Wride M, Lockwood D, Lee J, Lee Y. Anterior Versus Lateral Skull Base Fractures: Differences in Hospital Course and Need for Surgery. FACE 2024;5(3):517 View
  27. 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
  28. Mittal A, Afsar A, Tayal A, Shetty M. Artificial intelligence and healthcare. MAMC Journal of Medical Sciences 2023;9(2):81 View

Books/Policy Documents

  1. Li T, Yin N, Gao P, Li D, Lu W. Data Mining and Big Data. View
  2. Jana S, Dasgupta T, Dey L. Multimodal AI in Healthcare. View
  3. Shaban-Nejad A, Michalowski M, Bianco S. AI for Disease Surveillance and Pandemic Intelligence. View
  4. de Souza A, Ferreira F, Lambrecht R, Reichow L, Santos H, Reiser R, Yamin A. Intelligent Systems. View
  5. Abdullah Z, Ismail W, Zakaria L, Ismail S, Abdullah A. Data Science and Emerging Technologies. View
  6. El Sherbini A, Glicksberg B, Krittanawong C. Artificial Intelligence in Clinical Practice. View
  7. Singh P, Kansal M, Lahiri S, Vishnoi H, Mittal L. Enhancing Medical Imaging with Emerging Technologies. View
  8. Ray P, Sharma S, Rawal R, Shah D. Modeling and Optimization of Signals Using Machine Learning Techniques. View
  9. Touati Hamad Z, Laouar M, Dhouha G. 13th International Conference on Information Systems and Advanced Technologies “ICISAT 2023”. View