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Emergency Department Length of Stay Classification Based on Ensemble Methods and Rule Extraction.
Aziz, Waqar; Nicalaou, Andria; Stylianides, Charithea; Panayides, Andreas; Kakas, Antonis; Kyriacou, Efthyvoulous; Pattichis, Constantinos.
Afiliación
  • Aziz W; Biomedical Engineering Research Centre, University of Cyprus, Cyprus.
  • Nicalaou A; Biomedical Engineering Research Centre, University of Cyprus, Cyprus.
  • Stylianides C; CYENS Centre of Excellence, Nicosia, Cyprus.
  • Panayides A; CYENS Centre of Excellence, Nicosia, Cyprus.
  • Kakas A; CYENS Centre of Excellence, Nicosia, Cyprus.
  • Kyriacou E; Biomedical Engineering Research Centre, University of Cyprus, Cyprus.
  • Pattichis C; CYENS Centre of Excellence, Nicosia, Cyprus.
Stud Health Technol Inform ; 316: 1812-1816, 2024 Aug 22.
Article en En | MEDLINE | ID: mdl-39176843
ABSTRACT
This study employs machine learning techniques to identify factors that influence extended Emergency Department (ED) length of stay (LOS) and derives transparent decision rules to complement the results. Leveraging a comprehensive dataset, Gradient Boosting exhibited marginally superior predictive performance compared to Random Forest for LOS classification. Notably, variables like triage acuity and the Elixhauser Comorbidity Index (ECI) emerged as robust predictors. The extracted rules optimize LOS stratification and resource allocation, demonstrating the critical role of data-driven methodologies in improving ED workflow efficiency and patient care delivery.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Servicio de Urgencia en Hospital / Aprendizaje Automático / Tiempo de Internación Límite: Humans Idioma: En Revista: Stud Health Technol Inform Asunto de la revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Año: 2024 Tipo del documento: Article País de afiliación: Chipre Pais de publicación: Países Bajos
Texto completo
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Imprimir
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Servicio de Urgencia en Hospital / Aprendizaje Automático / Tiempo de Internación Límite: Humans Idioma: En Revista: Stud Health Technol Inform Asunto de la revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Año: 2024 Tipo del documento: Article País de afiliación: Chipre Pais de publicación: Países Bajos