Integrating data mining and optimization techniques on surgery scheduling

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2012

Autores

Gomes, C (Autor) Outra; Bernardo Almada Lobo (Autor) FEUP; José Luís Moura Borges (Autor) FEUP; Carlos Soares (Autor) FEP

Ata de Conferência Internacional

Título: Advanced Data Mining and Applications

Páginas: 589-602

Indexação

Scopus - 14 Citações

Outras Informações

ID Authenticus: P-008-8RZ

DOI: 10.1007/978-3-642-35527-1_49

Abstract (EN): This paper presents a combination of optimization and data mining techniques to address the surgery scheduling problem. In this approach, we first develop a model to predict the duration of the surgeries using a data mining algorithm. The prediction model outcomes are then used by a mathematical optimization model to schedule surgeries in an optimal way. In this paper, we present the results of using three different data mining algorithms to predict the duration of surgeries and compare them with the estimates made by surgeons. The results obtained by the data mining models show an improvement in estimation accuracy of 36%.We also compare the schedules generated by the optimization model based on the estimates made by the prediction models against reality. Our approach enables an increase in the number of surgeries performed in the operating theater, thus allowing a reduction on the average waiting time for surgery and a reduction in the overtime and undertime per surgery performed. These results indicate that the proposed approach can help the hospital improve significantly the efficiency of resource usage and increase the service levels. © Springer-Verlag 2012.

Idioma: Inglês

Tipo (Avaliação Docente): Científica