Hypertensive disorders are the most common problems during pregnancy. They cause about 10% of maternal deaths. The world mortality rate has decreased but many women are still dying every day from pregnancy complications. Various technic resources are being used in an integrated manner in order to minimize even more the death of both mothers and babies. Mobile devices with Internet access have a great potential to expand actions of health professionals. These devices facilitate care with people that are living in remote areas, assisting in patient monitoring. Information exchange anywhere and anytime between experts and patients could be an important way to improve the pregnancy monitoring. This paper presents a mobile monitoring solution using body sensors to identify worsens in the health status of pregnant women suffering hypertensive disorders. This mobile application uses Naïve Bayes classifier to better identify hypertension severity helping experts in decisionmaking process. Results show that the proposed mobile system is promising for monitoring blood pressure disorders in pregnancy.
Resumo O estudo objetivou desenvolver um protótipo da Aplicação GISSA ChatBot Mamãe-Bebê (GCBMB), um agente conversacional voltado à promoção da saúde infantil, assim como avaliar a experiência de uso e a satisfação com a referida solução tecnológica. Trata-se de uma pesquisa transversal, de metodologia mista, em duas etapas: a 1ª de desenvolvimento dos cenários de diálogo e do protótipo do GCBMB, e, a 2ª, de avaliação da experiência do uso do ChatBot por meio de um questionário estruturado, e análise do percurso de uso da Aplicação através de seu banco de dados. A amostra foi de 142 mulheres puérperas, com idade média de 25,4 anos, onde 38,1% eram primíparas. O nível de concordância das mulheres com a simplicidade, boa qualidade da informação, clareza do conteúdo, utilidade e satisfação com a aplicação, estiveram acima de 90%. Mulheres entre 26 a 30 anos apresentaram maiores médias de quantidade de acessos (5,21), quantidade de cenários acessados (9,26) e tempo de uso (272 segundos) comparando-se as mais jovens e as mais velhas. O uso de ChatBots em smartphones é animador para promoção da saúde das crianças, porém são necessários mais investimentos para o aperfeiçoamento de soluções tecnológicas e pesquisas com metodologias robustas para avaliar a sua efetividade.
Multiple wearable devices for cardiovascular self-monitoring have been proposed over the years, with growing evidence showing their effectiveness in the detection of pathologies that would otherwise be unnoticed through standard routine exams. In particular, Electrocardiography (ECG) has been an important tool for such purpose. However, wearables have known limitations, chief among which are the need for a voluntary action so that the ECG trace can be taken, battery lifetime, and abandonment. To effectively address these, novel solutions are needed, which has recently paved the way for “invisible” (aka “off-the-person”) sensing approaches. In this article we describe the design and experimental evaluation of a system for invisible ECG monitoring at home. For this purpose, a new sensor design was proposed, novel materials have been explored, and a proof-of-concept data collection system was created in the form of a toilet seat, enabling ECG measurements as an extension of the regular use of sanitary facilities, without requiring body-worn devices. In order to evaluate the proposed approach, measurements were performed using our system and a gold standard equipment, involving 10 healthy subjects. For the acquisition of the ECG signals on the toilet seat, polymeric electrodes with different textures were produced and tested. According to the results obtained, some of the textures did not allow the acquisition of signals in all users. However, a pyramidal texture showed the best results in relation to heart rate and ECG waveform morphology. For a texture that has shown 0% signal loss, the mean heart rate difference between the reference and experimental device was − 1.778 ± 4.654 Beats per minute (BPM); in terms of ECG waveform, the best cases present a Pearson correlation coefficient above 0.99.
GISSA é um sistema inteligente para a tomada de decisões em saúde focado no cuidado materno infantil. Neste sistema, vários alertas são gerados nos cinco domínios da saúde (clínico-epidemiológico, normativo, administrativo, gestão do conhecimento, conhecimento compartilhado). O sistema se propõe a contribuir para a redução da mortalidade infantil no Brasil. Este artigo apresenta o LAIS, um mecanismo inteligente que usa aprendizado de máquina para gerar alertas de risco de mortalidade infantil no GISSA. Para tanto, este trabalho usa uma metodologia baseada na mineração de dados para alcançar um modelo de aprendizagem capaz de calcular a probabilidade de um recém-nascido morrer. Os testes mostram que o classificador Naive Bayes é o mais adequado para este propósito, apresentando bons resultados, com área da curva ROC de 92,1%. O trabalho reúne bases de dados do Ministério da Saúde, SIM e SINASC, para o treinamento de algoritmos de classificação, identificando relações entre dados de nascimento e de morte de crianças com menos de um an. Durante o processo metodológico foi utilizado o algoritmo spread subsample, que aplica sub-amostragem, melhorando os resultados do modelo.
Smart decision support systems (DSSs) have been successfully employed in several areas. In healthcare, these systems offer solutions for uncertain reliably acts and moments. Systems based on Bayesian networks (BNs) can generate predictions even in information lack situations. This paper proposes the modeling and presents a performance evaluation study of the Bayesian classifier named Tree Augmented Na¨ıve Bayes (TAN). Results show that the proposed algorithm obtained good performance for a pregnancy database, presenting F-measure 0.92, Kappa statistic 0.8932, and ROC area 0.993. The proposed method allows representing more complex connections between variables. Nevertheless, it requires major computational effort and time that are not needed in other Bayesian algorithms.
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