Open Access
SHS Web Conf.
Volume 194, 2024
The 6th ETLTC International Conference on ICT Integration in Technical Education (ETLTC2024)
Article Number 01005
Number of page(s) 9
Section Intelligent Applications in Society
Published online 26 June 2024
  1. Gutierrez, G. (2020). Artificial intelligence in the intensive care unit. Annual Update in Intensive Care and Emergency Medicine 2020, 667–681. [CrossRef] [Google Scholar]
  2. Escobar GJ, Turk BJ, Ragins A, et al. Piloting electronic medical record-based early detection of inpatient deterioration in community hospitals. J Hosp Med. 2016;11(Suppl 1):S18–24. [Google Scholar]
  3. Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319:1317–8. [CrossRef] [Google Scholar]
  4. Houthooft R, Ruyssinck J, van der Herten J, et al. Predictive modeling of survival and length of stay in critically ill patients using sequential organ failure scores. Artif Intell Med. 2015;63:191–207. [CrossRef] [Google Scholar]
  5. Awad A, Bader-El-Den M, McNicholas J, Briggs J. Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach. Int J Med Inform. 2017;108:185–95. [CrossRef] [Google Scholar]
  6. Alghatani, K., Ammar, N., Rezgui, A., & Shaban-Nejad, A. (2021). Predicting intensive care unit length of stay and mortality using patient vital signs: machine learning model development and validation. JMIR medical informatics, 9(5), e21347. [CrossRef] [Google Scholar]
  7. Atallah, L., Nabian, M., Brochini, L., & Amelung, P. J. (2023). Machine Learning for Benchmarking Critical Care Outcomes. Healthcare Informatics Research, 29(4), 301. [CrossRef] [Google Scholar]
  8. Vagliano, I., Dormosh, N., Rios, M., Luik, T. T., Buonocore, T. M., Elbers, P. W. G., … & Abu-Hanna, A. (2023). Prognostic models of in-hospital mortality of intensive care patients using neural representation of unstructured text: a systematic review and critical appraisal. Journal of Biomedical Informatics, 104504. [CrossRef] [Google Scholar]
  9. Parreco, J., Hidalgo, A., Kozol, R., Namias, N., & Rattan, R. (2018). Predicting mortality in the surgical intensive care unit using artificial intelligence and natural language processing of physician documentation. The American Surgeon, 84(7), 1190–1194. [Google Scholar]
  10. Krishnan, G. S. (2019, January). Evaluating the quality of word representation models for unstructured clinical text based ICU mortality prediction. In Proceedings of the 20th International Conference on Distributed Computing and Networking (pp. 480–485). [Google Scholar]
  11. Ruminski, C. M., Clark, M. T., Lake, D. E., Kitzmiller, R. R., Keim-Malpass, J., Robertson, M. P., … & Calland, J. F. (2019). Impact of predictive analytics based on continuous cardiorespiratory monitoring in a surgical and trauma intensive care unit. Journal of clinical monitoring and computing, 33, 703–711. [CrossRef] [Google Scholar]
  12. Chang, D., Chang, D., & Pourhomayoun, M. (2019, December). Risk prediction of critical vital signs for ICU patients using recurrent neural network. In 2019 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 1003–1006). IEEE. [Google Scholar]
  13. Yu, R., Zheng, Y., Zhang, R., Jiang, Y., & Poon, C. C. (2019). Using a multi-task recurrent neural network with attention mechanisms to predict hospital mortality of patients. IEEE journal of biomedical and health informatics, 24(2), 486–492. [Google Scholar]
  14. Cheng, T. Y., Ho, S. Y. C., Chien, T. W., & Chou, W. (2023). Global research trends in artificial intelligence for critical care with a focus on chord network charts: Bibliometric analysis. Medicine, 102(38), e35082. [CrossRef] [Google Scholar]
  15. Alanazi, A., Aldakhil, L., Aldhoayan, M., & Aldosari, B. (2023). Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients. Medicina, 59(7), 1276. [CrossRef] [Google Scholar]
  16. Desautels, T., Calvert, J., Hoffman, J., Jay, M., Kerem, Y., Shieh, L., … & Das, R. (2016). Prediction of sepsis in the intensive care unit with minimal electronic health record data: a machine learning approach. JMIR medical informatics, 4(3), e5909. [Google Scholar]

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