Cargando…
Prognosis prediction in traumatic brain injury patients using machine learning algorithms
Predicting treatment outcomes in traumatic brain injury (TBI) patients is challenging worldwide. The present study aimed to achieve the most accurate machine learning (ML) algorithms to predict the outcomes of TBI treatment by evaluating demographic features, laboratory data, imaging indices, and cl...
Autores principales: | Khalili, Hosseinali, Rismani, Maziyar, Nematollahi, Mohammad Ali, Masoudi, Mohammad Sadegh, Asadollahi, Arefeh, Taheri, Reza, Pourmontaseri, Hossein, Valibeygi, Adib, Roshanzamir, Mohamad, Alizadehsani, Roohallah, Niakan, Amin, Andishgar, Aref, Islam, Sheikh Mohammed Shariful, Acharya, U. Rajendra |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9849475/ https://www.ncbi.nlm.nih.gov/pubmed/36653412 http://dx.doi.org/10.1038/s41598-023-28188-w |
Ejemplares similares
-
Body composition predicts hypertension using machine learning methods: a cohort study
por: Nematollahi, Mohammad Ali, et al.
Publicado: (2023) -
The role of vitamin D receptor and IL‐6 in COVID‐19
por: Azmi, Ali, et al.
Publicado: (2023) -
Editorial: Contemporary causes of acute myocarditis and pericarditis: diagnosis by advanced imaging techniques and therapeutic strategies
por: Korosoglou, Grigorios, et al.
Publicado: (2023) -
Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images
por: Sharifrazi, Danial, et al.
Publicado: (2021) -
Application of Brain Perfusion SPECT in the Evaluation of Response to Zolpidem Therapy in Consciousness Disorder Due to Traumatic Brain Injury
por: Khalili, Hosseinali, et al.
Publicado: (2020)