LARIISA: an intelligent platform to help decision makers in the brazilian health public system
Publication:WebMedia ’19: Proceedings of the 25th Brazillian Symposium on Multimedia and the WebOctober 2019 https://doi.org/10.1145/3323503.3362122
LARIISA is an intelligent framework for decision-making in public health systems. The project had its initial ideas conceived in 2009. Since then it has evolved in the academic and market perspective, becoming a product in 2018 called GISSA. This article presents the architectural evolution of LARIISA, the functionalities implemented, the scientific and commercial results achieved with GISSA. Ontology and Data Mining (DM) are technologies that support their inference mechanisms. A semantic portal is proposed for GISSA and a DM application is presented.
Using Linked Data in the Data Integration for Maternal and Infant Death Risk of the SUS in the GISSA Project
- Published in: WebMedia – XVI Workshop de Ferramentas Aplicações (17-20 October)
- DOI: 10.1145/3126858.3131606 – Conf Location: Gramado, RGS – Brazil
- ACM New York, NY, USA ©2017 – table of contents ISBN: 978-1-4503-5096-9
Making good governance decisions is a constant challenge for Public Health administration. Health managers need to make data analysis in order to identify several health problems. In Brazil, these data are made available by DATASUS. Generally, they are stored in distinct and heterogeneous databases. TheLinked Data approach allow a homogenized view of the data as a unique basis. This article proposes a ontology-based model andLinked Data to integrate datasets and calculate the probability of maternal and infant death risk in order to give support in decision-making in the GISSA project.
Performance Evaluation od Predictive Classifiers for Pregnancy Care
Date of Conference: 4-8 Dec. 2016
Date Added to IEEE Xplore: 06 February 2017
INSPEC Accession Number: 16654639
Conference Location: Washington, DC, USA
Hypertensive disorders are the leading cause of deaths during pregnancy. Risk pregnancy accompaniment is essential to reduce these complications. Decision support systems (DSS) are important tools to patients’ accompaniment. These systems provide relevant information to health experts about clinical condition of the patient anywhere and anytime. In this paper, a model that uses the Naive Bayesian classifier is introduced and its performance is evaluated in comparison with the Data Mining (DM) classifier named J48 Decision Tree. This study includes the modeling, performance evaluation, and comparison between models that could be used to assess pregnancy complications. Evaluation analysis of the results is performed through the use of Confusion Matrix indicators. The founded results show that J48 decision tree classifier performs better for almost all the used indicators, confirming its promising accuracy for identifying hypertensive disorders on pregnancy.