Cargando…
Physiologic signatures within six hours of hospitalization identify acute illness phenotypes
During the early stages of hospital admission, clinicians use limited information to make decisions as patient acuity evolves. We hypothesized that clustering analysis of vital signs measured within six hours of hospital admission would reveal distinct patient phenotypes with unique pathophysiologic...
Autores principales: | Ren, Yuanfang, Loftus, Tyler J., Li, Yanjun, Guan, Ziyuan, Ruppert, Matthew M., Datta, Shounak, Upchurch, Gilbert R., Tighe, Patrick J., Rashidi, Parisa, Shickel, Benjamin, Ozrazgat-Baslanti, Tezcan, Bihorac, Azra |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9802629/ https://www.ncbi.nlm.nih.gov/pubmed/36590701 http://dx.doi.org/10.1371/journal.pdig.0000110 |
Ejemplares similares
-
Dynamic predictions of postoperative complications from explainable, uncertainty-aware, and multi-task deep neural networks
por: Shickel, Benjamin, et al.
Publicado: (2023) -
Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Predict Postoperative Complications and Report on a Mobile Platform
por: Ren, Yuanfang, et al.
Publicado: (2022) -
Machine Learning–Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review
por: Balch, Jeremy A, et al.
Publicado: (2023) -
Building an automated, machine learning-enabled platform for predicting post-operative complications
por: Balch, Jeremy A, et al.
Publicado: (2023) -
DeepSOFA: A Continuous Acuity Score for Critically Ill Patients using Clinically Interpretable Deep Learning
por: Shickel, Benjamin, et al.
Publicado: (2019)