Observatorio de I+D+i UPM

Memorias de investigación
Artículos en revistas:
Emergency Department Visit Forecasting and Dynamic Nursing Staff Allocation Using Machine Learning Techniques With Readily Available Open-Source Software
Año:2015
Áreas de investigación
  • Tecnología electrónica y de las comunicaciones,
  • Ingeniería eléctrica, electrónica y automática
Datos
Descripción
Although emergency department visit forecasting can be of use for nurse staff planning, previous research has focused on models that lacked sufficient resolution and realistic error metrics for these predictions to be applied in practice. Using data from a 1100-bed specialized care hospital with 553,000 patients assigned to its healthcare area, forecasts with different prediction horizons, from 2 to 24 weeks ahead, with an 8-hour granularity, using support vector regression, M5P, and stratified average time-series models were generated with an open-source software package. As overstaffing and understaffing errors have different implications, error metrics and potential personnel monetary savings were calculated with a custom validation scheme, which simulated subsequent generation of predictions during a 4-year period. Results were then compared with a generalized estimating equation regression. Support vector regression and M5P models were found to be superior to the stratified average model with a 95% confidence interval. Our findings suggest that medium and severe understaffing situations could be reduced in more than an order of magnitude and average yearly savings of up to ?683,500 could be achieved if dynamic nursing staff allocation was performed with support vector regression instead of the static staffing levels currently in use.
Internacional
Si
JCR del ISI
Si
Título de la revista
Cin-Computers Informatics Nursing
ISSN
1538-2931
Factor de impacto JCR
0,758
Información de impacto
Volumen
33
DOI
10.1097/CIN.0000000000000173
Número de revista
8
Desde la página
368
Hasta la página
377
Mes
SIN MES
Ranking
Ranking in category 111/76
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Alexander Zlotnik Enaliev (UPM)
  • Autor: Juan Manuel Montero Martinez (UPM)
  • Autor: Ascensión Gallardo Antolín (Universidad Carlos III Madrid - Signal Theory and Communications Department)
  • Autor: Miguel Cuchí Alfaro (Hospital Universitario Ramón y Cajal, Madrid)
  • Autor: María Carmen Pérez Pérez
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de Tecnología del Habla
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