Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/51665

TitleKnowledge discovery from surgical waiting lists
Author(s)Neto, Cristiana
Peixoto, Hugo Daniel Abreu
Abelha, Vasco António Pinheiro Costa
Abelha, António
Machado, José Manuel
KeywordsKnowledge Discovery in Databases
Data mining
Surgery waiting list
Decision Support Systems
Classification
Issue date2017
PublisherElsevier
JournalProcedia Computer Science
Abstract(s)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.
TypeConference paper
URIhttp://hdl.handle.net/1822/51665
DOI10.1016/j.procs.2017.11.141
ISSN1877-0509
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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