Autor: lariisasd

Resumo: O objetivo deste estudo foi investigar o processo de colaboração interprofissional entre os diretores, docentes de instituições de ensino superior (IES), gestores dos Sistemas Municipais de Saúde e profissionais da Estratégia de Saúde da Família de duas cidades estratégicas para expansão do ensino superior em saúde no Ceará. Tratou-se de estudo analítico de casos múltiplos. Foram utilizadas pesquisa documental e entrevistas semi-estruturadas com 75 gestores e profissionais da saúde, diretores e docentes de IES.

Abstract: GISSA is an intelligent system for health decision making focused on childish maternal care. In this system, are generated alerts that involve the five health domains: clinicalepidemiological, normative, administrative, knowledge management and shared knowledge. The system proposes to contribute to the reduction of child mortality in Brazil. Thus, this paper presents studies over an intelligent module that uses Machine Learning to generate child death risk alerts on GISSA. These studies focus on trying different classification Algorithms, with a methodology based on Data Mining to reach a learning model capable of calculating the probability of a newborn dying. The work brings together public databases SIM and SINASC for the training of classification algorithms, identifying relationships between birth and death data of children under one year. During the methodological process, it was made a subsampling to balance the number of inputs and be fair in the training model results, executed with Matlab scripts.

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.

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.

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.

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.

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.

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.

Abstract: The home care consists in a form of primary care performed by a lay caregiver, a specialist or a multidisciplinary team. This modality is applied in elderly people or patients in treatment of chronic disease who are not at risk of death. The aim of this work is to present a set of context-aware health applications in a prototype of software and hardware that will assist caregivers and/or patients in home care situations. For this, a Set-Top Box (STB) connected to a TV with access to the Internet is used as a way of user interaction, which may enter information about its current state. Furthermore, health sensors can be used to capture data continuously to feed the system. The raw data and information provided by the user are later used, allowing, then, an inference about the patient condition.

Abstract: Health care services can be scarce and expensive in some countries and especially in isolated regions. The lack of information can degrade health care services, for example, by ineffective resource allocation or failure in epidemiological prediction. This paper proposes an architecture for system of decision making and service provisioning in the health care context. It encompasses and integrates data produced by environmental sensors installed in the assisted homes, medical data sets, domainspecific and semantic enriched data sets, and all data generated and collected in applications installed on mobile phones, wearable devices, desktops, web servers, and smart television. LARIISA architecture is presented as a platform to manage, provide and launch services that monitor and analyze data to supply relevant information to decision makers and health care actors that participate in the health care supply chain