Knowledge discovery from surgical waiting lists

• Autor(a) principal: Neto, Cristiana
• Data de Publicação: 2017
• Outros Autores: Peixoto, Hugo Daniel Abreu, Abelha, Vasco António Pinheiro Costa, Abelha, António, Machado, José Manuel
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/1822/51665
• topic: Knowledge Discovery in Databases; Data mining; Surgery waiting list; Decision Support Systems; Classification; Science & Technology

Methods for knowledge discovery in data bases (KDD) have been studied for more than a decade. New methods are required owing to the size and complexity of data collections in administration, business and science. They include procedures for data query and extraction, for data cleaning, data analysis, and methods of knowledge representation. The part of KDD dealing with the analysis of the data has been termed data mining. Data mining is the process of pattern discovery and extraction where huge amount of data is involved. Data mining has been used intensively and extensively by many organizations. In healthcare, data mining is becoming increasingly popular, if not increasingly essential. Data mining applications can greatly benefit all parties involved in the healthcare industry. In this work is presented an approach for the use of data mining in the context of waiting lists for surgery, namely for predicting the type of surgery (programmed or additional) for a record in the list.