Cargando…
Predicting Adult Hospital Admission from Emergency Department Using Machine Learning: An Inclusive Gradient Boosting Model
Background and aim: We analyzed an inclusive gradient boosting model to predict hospital admission from the emergency department (ED) at different time points. We compared its results to multiple models built exclusively at each time point. Methods: This retrospective multisite study utilized ED dat...
Autores principales: | Patel, Dhavalkumar, Cheetirala, Satya Narayan, Raut, Ganesh, Tamegue, Jules, Kia, Arash, Glicksberg, Benjamin, Freeman, Robert, Levin, Matthew A., Timsina, Prem, Klang, Eyal |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9740100/ https://www.ncbi.nlm.nih.gov/pubmed/36498463 http://dx.doi.org/10.3390/jcm11236888 |
Ejemplares similares
-
A Hybrid Decision Tree and Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation Among Hospitalized Patients With COVID-19: Algorithm Development and Validation
por: Nguyen, Kim-Anh-Nhi, et al.
Publicado: (2023) -
Predicting adult neuroscience intensive care unit admission from emergency department triage using a retrospective, tabular-free text machine learning approach
por: Klang, Eyal, et al.
Publicado: (2021) -
Synergistic effect of hypoalbuminaemia and hypotension in predicting in-hospital mortality and intensive care admission: a retrospective cohort study
por: Klang, Eyal, et al.
Publicado: (2021) -
MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model
por: Kia, Arash, et al.
Publicado: (2020) -
Obesity as a mortality risk factor in the medical ward: a case control study
por: Soffer, Shelly, et al.
Publicado: (2022)