Cargando…
Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 H Post-ICU Admission
A complicated clinical course for critically ill patients admitted to the intensive care unit (ICU) usually includes multiorgan dysfunction and subsequent death. Owing to the heterogeneity, complexity, and unpredictability of the disease progression, ICU patient care is challenging. Identifying the...
Autores principales: | Banerjee, Shayantan, Mohammed, Akram, Wong, Hector R., Palaniyar, Nades, Kamaleswaran, Rishikesan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7937924/ https://www.ncbi.nlm.nih.gov/pubmed/33692779 http://dx.doi.org/10.3389/fimmu.2021.592303 |
Ejemplares similares
-
Identification of a pediatric acute hypoxemic respiratory failure signature in peripheral blood leukocytes at 24 hours post-ICU admission with machine learning
por: Grunwell, Jocelyn R., et al.
Publicado: (2023) -
Altered Heart Rate Variability Early in ICU Admission Differentiates Critically Ill Coronavirus Disease 2019 and All-Cause Sepsis Patients
por: Kamaleswaran, Rishikesan, et al.
Publicado: (2021) -
A MACHINE LEARNING MODEL DERIVED FROM ANALYSIS OF TIME-COURSE GENE-EXPRESSION DATASETS REVEALS TEMPORALLY STABLE GENE MARKERS PREDICTIVE OF SEPSIS MORTALITY
por: Huang, Min, et al.
Publicado: (2023) -
Bioenergetic Crisis in ICU-Acquired Weakness Gene Signatures Was Associated With Sepsis-Related Mortality: A Brief Report
por: Kobara, Seibi, et al.
Publicado: (2022) -
Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU
por: Sikora, Andrea, et al.
Publicado: (2023)