Paper Info
Title
Clustering-based Approach for Categorizing Pregnant Women in Obstetrics and Maternity Care
Abstract
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.
Year
DOI
Venue
2015
10.1145/2790798.2790814
IEEE International Conference on Computational Science and Engineering
Field
DocType
Citations 
Interoperability,Computer science,Decision support system,Obstetrics,Cluster analysis
Conference
2
PageRank 
References 
Authors
0.71
3
5
Name
Order
Citations
PageRank
Sónia Pereira141.42
Filipe Portela217744.10
Manuel Filipe Santos336068.91
José Machado420734.92
António Abelha524357.30