Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/39264

TitleClustering-based approach for categorizing pregnant women in obstetrics and maternity care
Author(s)Pereira, Sónia
Portela, Filipe
Santos, Manuel Filipe
Machado, José Manuel
Abelha, António
KeywordsData mining
Clustering
Real data
Interoperability
Decision support systems
Obstetrics care
Maternity care
Issue date2015
PublisherACM
Abstract(s)When a pregnant woman is guided to a hospital for obstetrics purposes, many outcomes are possible, depending on her current conditions. An improved understanding of these conditions could provide a more direct medical approach by categorizing the different types of patients, enabling a faster response to risk situations, and therefore increasing the quality of services. In this case study, the characteristics of the patients admitted in the maternity care unit of Centro Hospitalar of Porto are acknowledged, allowing categorizing the patient women through clustering techniques. The main goal is to predict the patients’ route through the maternity care, adapting the services according to their conditions, providing the best clinical decisions and a cost-effective treatment to patients. The models developed presented very interesting results, being the best clustering evaluation index: 0.65. The evaluation of the clustering algorithms proved the viability of using clustering based data mining models to characterize pregnant patients, identifying which conditions can be used as an alert to prevent the occurrence of medical complications.
TypeConference paper
URIhttp://hdl.handle.net/1822/39264
ISBN978-1-4503-3419-8
DOI10.1145/2790798.2790814
Publisher versionhttp://dl.acm.org/citation.cfm?id=2790814
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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