Cargando…
Longitudinal validation of an electronic health record delirium prediction model applied at admission in COVID-19 patients
OBJECTIVE: To validate a previously published machine learning model of delirium risk in hospitalized patients with coronavirus disease 2019 (COVID-19). METHOD: Using data from six hospitals across two academic medical networks covering care occurring after initial model development, we calculated t...
Autores principales: | Castro, Victor M., Hart, Kamber L., Sacks, Chana A., Murphy, Shawn N., Perlis, Roy H., McCoy, Thomas H. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Inc.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8562039/ https://www.ncbi.nlm.nih.gov/pubmed/34798580 http://dx.doi.org/10.1016/j.genhosppsych.2021.10.005 |
Ejemplares similares
-
Development and External Validation of a Delirium Prediction Model for Hospitalized Patients With Coronavirus Disease 2019
por: Castro, Victor M., et al.
Publicado: (2021) -
Case-control study of neuropsychiatric symptoms in electronic health records following COVID-19 hospitalization in 2 academic health systems
por: Castro, Victor M., et al.
Publicado: (2022) -
Stratification of risk for hospital admissions for injury related to fall: cohort study
por: Castro, Victor M, et al.
Publicado: (2014) -
Sentiment Measured in Hospital Discharge Notes Is Associated with Readmission and Mortality Risk: An Electronic Health Record Study
por: McCoy, Thomas H., et al.
Publicado: (2015) -
Laboratory Findings Associated With Severe Illness and Mortality Among Hospitalized Individuals With Coronavirus Disease 2019 in Eastern Massachusetts
por: Castro, Victor M., et al.
Publicado: (2020)