Publicação
Forecasting weekly emergency department demand in a Portuguese private hospital
| Resumo: | The overcrowding phenomenon is a worldwide problem that has been negatively affecting both public and private hospitals. A suitable and efficient planning of ED resources may diminish the effects of this event. Therefore, a Linear Regression, SARIMAX and Long-Short Term Memory models were developed to forecast weekly ED arrivals. Based on a Machine Learning multi-step ahead predictive tool to help in the decision-making process, the hospital may ensure a good quality of services. First, the predictive tool was used to forecast weekly ED demand for all patients in a big unit of a private Portuguese healthcare provider, CUF, and then, to predict the Urgent Patients weekly ED arrivals for the same unit. |
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| Autores principais: | Alfaro, Miguel Alexandre Rocha Martins |
| Assunto: | Healthcare Emergency department Machine learning Time series Multi-step-ahead forecasting |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | The overcrowding phenomenon is a worldwide problem that has been negatively affecting both public and private hospitals. A suitable and efficient planning of ED resources may diminish the effects of this event. Therefore, a Linear Regression, SARIMAX and Long-Short Term Memory models were developed to forecast weekly ED arrivals. Based on a Machine Learning multi-step ahead predictive tool to help in the decision-making process, the hospital may ensure a good quality of services. First, the predictive tool was used to forecast weekly ED demand for all patients in a big unit of a private Portuguese healthcare provider, CUF, and then, to predict the Urgent Patients weekly ED arrivals for the same unit. |
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