Identifying low acuity Emergency Department visits with a machine learning approach: The low acuity visit algorithms (LAVA).

Journal: Health services research

PMID: 38553999

Abstract

OBJECTIVE: To improve the performance of International Classification of Disease (ICD) code rule-based algorithms for identifying low acuity Emergency Department (ED) visits by using machine learning methods and additional covariates.

Authors

Angela T Chen

Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Richard S Kuzma

Emergency Medicine Department, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Ari B Friedman

Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Keywords

Adolescent Adult Age Factors Aged Algorithms Child Child, Preschool Emergency Room Visits Emergency Service, Hospital Female Humans International Classification of Diseases Logistic Models Machine Learning Male Middle Aged Patient Acuity Sex Factors United States Young Adult

External Resources

View on PubMed Access via DOI PubMed (38553999)

Identifying low acuity Emergency Department visits with a machine learning approach: The low acuity visit algorithms (LAVA).

Abstract

Authors

Keywords

External Resources

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