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Despite the efforts of the Ministry of Health, the Brazilian public health service is still precarious, mainly due to the difficulty in accessing the patient's health history during the medical consultation. This paper proposes an intelligent health scenario that captures, enriches and provides clinical, regulatory, drug information and similar cases in real time in order to improve the quality of Primary Health Care. This scenario uses inferring mechanisms that allow the extraction and add value to relevant clinical information, helping physicians make better diagnoses and prescriptions. Its construction results from the integration of Wearable and Linked Data technologies in Electronic Health Records context.

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.

Several physical or emotional factors can contribute negatively to critical moments in the health area, negatively influencing the diagnosis of diseases. Therefore, this work proposes an intelligent solution based on classifiers as an inference mechanism capable of assisting health professionals during the process of clinical management of diseases transmitted by the Aedes Aegypti mosquito, identifying the most probable diagnosis based on symptoms and outcome of exams. Thus, two learning models capable of inferring the probability of a patient being infected with a particular disease were applied, with an accuracy up to 91.6%. An intelligent API to support decision-making was then built during the clinical management of dengue and chikungunya. The solution allows several applications to access learning models. As proof of concept, a mobile application of popular consultation for the identification of dengue and chikungunya was also developed.

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.

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.

Linked Data promotes the publication of structured data on the Web, easing the development of an homogeneized-view over heterogeneous sources, called Linked Data Mashup view (LDM view). But the development of this homogeneized view still is a challenging task. This article proposes a framework Ontology-based that aims to ease the process of creation of LDM views. This framework allow users without specifics knowledge to create their own applications, based on their needs. We also present a case study in which we use our approach to develop an integrated view over two heterogeneous sources from Brazilian Public Health System.