2017 / Artigos Científicos

Using Linked Data in the Data Integration for Maternal and Infant Death Risk of the SUS in the GISSA Project

Abstract: 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. The Linked Data approach allow a homogenized view of the data as a unique basis. This article proposes a ontology-based model and Linked 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.

2014 / Artigos Científicos

Using Bayesian Networks to improve the Decision Making Process in the Public Health System

Abstract – This paper proposes the use of Bayesian networks tosupport the decision-making process in health systems governance. In particular, this paper presents LARIISA_Bay, a new component based on Bayesian networks that works together with LARIISA, acontext-aware platform to support applications in public health systems. The main goal of the proposed component is to assist teamsof health specialists in order to better diagnose diseases through data collected from users of LARIISA. As a case study, we focus on scenarios of dengue fever disease. We classify dengue cases into oneof the following levels: emergency (i.e., dengue hemorrhagic fever),grave (i.e., dengue fever) or normal (i.e., absence of the disease). Based on this classification, teams of health specialists can accurately make decisions, for example, to alert a health care agent to visit locations with a high incidence of the disease, to send an ambulance when an dengue emergency case has occurred, as well as give technical instructions on how to deal with specific cases. We present a prototype of LARIISA_Bay and the corresponding interfaces to support the interactions of the patient, the health careagent and the specialist with the system.

2017 / Artigos Científicos

Using Predictive Classifiers to Prevent Infant Mortality in the Brazilian Northeast

Abstract: Despite the fact that infant mortality rates havebeen decreased in recent years, this issue stills being considered alarming to Brazilian health system indicators. In this context,the GISSA framework, an intelligent governance framework for Brazilian health system, emerges as a smart system for the Federal Government program, called Stork Network. Its main objective is to improve the healthcare for pregnant women as well as their newborns. This application aims to generate alerts focusing on the health status verification of newborns and pregnant woman to support decision-makers in preventive actions that may mitigate severe problems. Therefore, this paper presents the LAIS, an Intelligent health analysis system that uses data mining (DM) to generate newborns death risk alerts through probability-based methods. Results show that the NaıveBayes classifier presents better performance than the other DM approaches to the used pregnancy data set analysis of this work. This approach performed an accuracy of 0.982 and a Receiver Operating Characteristic (ROC) Area of 0.921. Both indicators suggest the proposed model may contribute to the reduction of maternal and fetal deaths.

2017 / Artigos Científicos

LAÍS, um Analisador Baseado em Classificadores para a Geração de Alertas Inteligentes em Saúde

Resumo: Tomar decisões de boa governança é um desafio constante para a administração da Saúde Pública. Os gestores de saúde precisam fazer análises de dados para identificar diversos problemas de saúde. No Brasil, esses dados são disponibilizados pelo DATASUS. Geralmente, eles são armazenados em bancos de dados distintos e heterogêneos. A abordagem Linked Data permite uma visão homogeneizada dos dados como uma base única. Este artigo propõe um modelo baseado em ontologia e Linked Data para integrar conjuntos de dados e calcular a probabilidade de risco de morte materna e infantil para dar suporte à tomada de decisão no projeto GISSA.

2016 / Artigos Científicos

Cross-cutting concerns: Improving an Intelligent System for Decision Making in Healthcare

Abstract: This paper proposes a better way to represent the architecture of LARIISA, an intelligent system for decision making in healthcare. The proposed representation weaves health and computational domains in a multidimensional architecture, which facilitates the visualization of specialized applications added to the LARIISA framework. New concepts as Big Data, Internet of Things and Linked Data are also introduced to the proposed architecture. Acquiring new data from different sources to the LARIISA database, and making use of it, will permit a more efficient decision-making process for the system.

2016 / Artigos Científicos

VITESSE – more intelligence with emerging technologies for health systems

Abstract: VITESSE is a low cost system to support users in two scenarios: home care and accidents (fainting, trampling, etc.). Initially, the system was based on the digital TV technology in scenarios of home care. Nowadays, the system adds new functions to support urgent and emergency care of individuals in mobility. In both cases, the key idea of VITESSE is to improve the time of consuming process, taking into account the real time and contextual information, in particular in the case of accidents of mobile users. Therefore, VITESSE is a context-aware system that makes use of the concept of Internet of Things (IoT) and ontologies in the process of generating inferences, increasing the efficiency of health care systems.