An efficient random forest algorithm-based telemonitoring framework to predict mortality and length of stay of patients in ICU
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17239-z.pdf
Reference36 articles.
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3. Wu W et al (2023) Developing and evaluating a machine-learning-based algorithm to predict the incidence and severity of ARDS with continuous non-invasive parameters from ordinary monitors and ventilators. Comput Methods Prog Biomed 230:107328
4. Ye Z et al (2023) The prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models. Eur J Med Res 28.1:1–13
5. Salman OH et al (2021) A review on utilizing machine learning technology in the fields of electronic emergency triage and patient priority systems in telemedicine: Coherent taxonomy, motivations, open research challenges and recommendations for intelligent future work. Comput Methods Prog Biomed 209:106357
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