Improving quality of medical service with mobile health software

An increasing number of m-Health applications are being developed benefiting health service delivery. In this paper, a new methodology based on the principle of calm computing applied to diagnostic and therapeutic procedure reporting is proposed. A mobile application was designed for the physicians of one of the Portuguese major hospitals, which takes advantage of a multi-agent interoperability platform, the Agency for the Integration, Diffusion and Archive (AIDA). This application allows the visualization of inpatients and outpatients medical reports in a quicker and safer manner, in addition to offer a remote access to information. This project shows the advantages in the use of mobile software in a medical environment but the first step is always to build or use an interoperability platform, flexible, adaptable and pervasive. The platform offers a comprehensive set of services that restricts the development of mobile software almost exclusively to the mobile user interface design. The technology was tested and assessed in a real context by intensivists.

Step towards a patient timeline in intensive care units

In Intensive Medicine, the presentation of medical information is done in many ways, depending on the type of data collected and stored. The way in which the information is presented can make it difficult for intensivists to quickly understand the patient's condition. When there is the need to cross between several types of clinical data sources the situation is even worse. This research seeks to explore a new way of presenting information about patients, based on the timeframe in which events occur. By developing an interactive Patient Timeline, intensivists will have access to a new environment in real-time where they can consult the patient clinical history and the data collected until the moment. The medical history will be available from the moment in which patients is admitted in the ICU until discharge, allowing intensivist to examine data regarding vital signs, medication, exams, among others. This timeline also intends to, through the use of information and models produced by the INTCare system, combine several clinical data in order to help diagnose the future patients’ conditions. This platform will help intensivists to make more accurate decision. This paper presents the first approach of the solution designed

Predicting type of delivery by identification of obstetric risk factors through data mining

In Maternity Care, a quick decision has to be made about the most suitable delivery type for the current patient. Guidelines are followed by physicians to support that decision; however, those practice recommendations are limited and underused. In the last years, caesarean delivery has been pursued in over 28% of pregnancies, and other operative techniques regarding specific problems have also been excessively employed. This study identifies obstetric and pregnancy factors that can be used to predict the most appropriate delivery technique, through the induction of data mining models using real data gathered in the perinatal and maternal care unit of Centro Hospitalar of Oporto (CHP). Predicting the type of birth envisions high-quality services, increased safety and effectiveness of specific practices to help guide maternity care decisions and facilitate optimal outcomes in mother and child. In this work was possible to acquire good results, achieving sensitivity and specificity values of 90.11% and 80.05%, respectively, providing the CHP with a model capable of correctly identify caesarean sections and vaginal deliveries.

Intelligent decision support to predict patient barotrauma risk in intensive care units

The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: “risk” and “no risk”. Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated.

Efeito dos óleos vegetais de andiroba (Carapa sp.) e Copaíba (Copaifera sp.) sobre forídeo, pragas de colméias, (Diptera: Phoridae) na Amazônia Central

O conhecimento de substâncias repelentes para forídeos é um passo importante para a meliponicultura brasileira, pois esses insetos podem causar sérios danos às colméias de abelhas nativas. Os óleos de copaíba e andiroba, naturalmente encontrados na região amazônica, são muito utilizados pelos povos tradicionais da região como repelentes de insetos. Foi observado o efeito de dois óleos vegetais (andiroba e copaíba) sobre a postura de ovos por fêmeas de forídeos em condições de laboratório. A postura das fêmeas foi realizada preferencialmente no substrato pólen e diferiu estatisticamente dos substratos contendo óleo de andiroba ou copaíba, nos quais houve considerável diminuição (até nenhuma postura), e do substrato contendo mel. Esses óleos são uma boa alternativa no controle preventivo e curativo dessa praga em colônias de Meliponineos, devido ao seu efeito repelente, ao baixo custo e disponibilidade na Região Amazônica.

A soft computing approach to kidney diseases evaluation

Kidney renal failure means that one’s kidney have unexpectedly stopped functioning, i.e., once chronic disease is exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapid deterioration of the renal function, but is often reversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis.The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow one to consider incomplete, unknown, and even contradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively.

Abstract computation in schizophrenia detection through artificial neural network based systems

Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information.

An assessment of chronic kidney diseases

Kidney renal failure means that one’s kidney have unexpectedlystoppedfunctioning,i.e.,oncechronicdiseaseis exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapiddeteriorationoftherenalfunction,butisoftenreversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis. The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow onetoconsiderincomplete,unknown,and evencontradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively.

Artificial neural networks in diagnosis of liver diseases

Liver diseases have severe patients’ consequences, being one of the main causes of premature death. These facts reveal the centrality of one`s daily habits, and how important it is the early diagnosis of these kind of illnesses, not only to the patients themselves, but also to the society in general. Therefore, this work will focus on the development of a diagnosis support system to these kind of maladies, built under a formal framework based on Logic Programming, in terms of its knowledge representation and reasoning procedures, complemented with an approach to computing grounded on Artificial Neural Networks.

Biologia reprodutiva de Cattleya eldorado, uma espécie de Orchidaceae das campinas amazônicas

As Orchidaceae são muito apreciadas por suas flores exóticas e exuberantes. É a maior família de plantas apresentando mais de 24000 espécies, o que denota uma alta diversidade de formas e adaptações a diferentes ambientes, como também para atração, engano e manipulação de visitantes na realização da polinização cruzada. Cattleya eldorado ocorre em áreas de campinas, que são formações vegetais típicas da região amazônica, que se encontram sob forte ação antrópica. Este trabalho tem como um de seus principais objetivos conhecer parte dos processos biológicos de C. eldorado fornecendo subsídios para conservá-la e manejá-la em seu habitat natural. Este estudo foi desenvolvido na Reserva Biológica de Campina, de 2000 a 2006, durante a sua floração. C. eldorado é uma espécie epifítica que apresenta a síndrome de melitofilia, estando adaptada ao seu polinizador, a abelha Eulaema mocsaryi, que reconhece suas flores pelo odor e pelo estímulo visual através de sua coloração e reflexão de luz ultravioleta. C. eldorado é uma espécie autocompatível, embora necessite de um agente polinizador para a transferência do polinário até sua deposição na cavidade estigmática da flor.

Sistema reprodutivo e polinização de Lepidagathis sessilifolia (Pohl) Kameyama ex Wassh. & J.R.I. Wood (Acanthaceae), em remanescente florestal da região sudoeste de Mato Grosso, Brasil

O gênero Lepidagathis apresenta distribuição pantropical com cerca de 100 espécies. No Brasil ocorrem 16 espécies, a maioria nas regiões Centro - Oeste e Sudeste. O estudo foi realizado em sub - bosque de remanescente florestal do município de Tangará da Serra - MT e teve como objetivo analisar a fenologia de floração, descrever a morfologia e biologia floral, verificar os visitantes florais e avaliar o sistema e o sucesso reprodutivo por meio de polinizações manuais. Lepidagathis sessilifolia apresenta inflorescências espiciformes, terminais, com cálice de cor rósea vistosa e corola de coloração branco-rósea. A floração ficou restrita aos meses de março a abril, durante a estação chuvosa. A senescência floral ocorreu após 24 ou 48 horas. A viabilidade dos grãos de pólen foi elevada (92,5%). O único polinizador observado visitando as flores de L. sessilifolia foi a abelha Partamona nhambiquara (Apidae - Meliponini). O sistema reprodutivo misto da espécie é caracterizado pela formação de frutos por meio de agamospermia, autopolinização e polinização cruzada. Esse sistema reprodutivo flexível é vantajoso, pois, garante a manutenção da espécie na área de estudo mesmo na ausência de polinizadores.

Abelhas-sem-ferrão amazônicas defendem meliponários contra saques de outras abelhas

Entre as abelhas eussociais, dois gêneros apresentam estratégia de vida cleptobiótica, obtendo recursos alimentares de ninhos de outras abelhas ao invés de coletá-los em flores. Entre as espécies atacadas existe um gradiente de suscetibilidade ao roubo variando desde espécies vulneráveis até altamente resistentes. Neste trabalho nós descrevemos um ataque de Lestrimelitta rufipes a um ninho de Scaptotrigona sp. em um meliponário na Amazônia central (Amazonas, Brazil). O ninho atacado foi transferido para um meliponário com espécies resistentes (Duckeola ghilianii e Melipona fulva) e as interações foram descritas. As abelhas resistentes contra-atacaram e afugentaram as ladras protegendo o ninho de Scaptotrigona sp.. A presença de comportamento defensivo em gêneros não proximamente relacionados sugere que ele tenha evoluído mais de uma vez entre os Meliponini. Considerando o comportamento descrito, sugerimos a criação de espécies nativas resistentes em meliponários de regiões onde elas forem nativas, devido ao potencial que elas tem na proteção.

Espectro polínico da própolis de Scaptotrigona aff. postica (Hymenoptera, Apidae, Meliponini) em Barra do Corda, MA, Brasil

A própolis é uma substância resinosa coletada por abelhas de diferentes partes de uma planta, portanto, sua qualidade é relacionada à sua origem botânica. O objetivo deste trabalho foi verificar se os tipos polínicos encontrados indicam a origem botânica de suas resinas e contribuir com o conhecimento da relação destas abelhas com a vegetação do entorno do meliponário. Foram realizadas 31 coletas ao longo de 12 meses, com intervalo de sete dias entre elas. Os grãos de pólen foram extraídos da própolis e utilizados para confecção de lâminas para microscopia, posteriormente procedeu-se a identificação, contabilização e determinação das classes de frequência. Foram encontrados 94 tipos polínicos oriundos de 35 famílias botânicas. Borreria verticillata (34,17%) foi o tipo polínico mais frequente, seguido por Anadenanthera sp. (13,65%) e Mimosa caesalpiniifolia (10,5%). Fabaceae (38,37%) e Rubiaceae (34,18%) foram as famílias que apresentaram as maiores frequências polínicas. Foram encontrados também 34 tipos polínicos exclusivos, ou seja tipos ocorrentes somente em um determinado mês do ano, podendo ser indicadores da caracterização sazonal de floração das espécies e assim inferir dados sobre sua fenologia. Os resultados obtidos não possibilitaram a determinação da origem botânica das resinas, entretanto a grande variedade de tipos polínicos encontrados nas amostras de própolis indicou uma ampla interação entre as abelhas e as plantas e contribuíram para a caracterização fitogeográfica da própolis.

Quality of life assessment of patients with schizophrenic spectrum disorders from Psychosocial Care Centers

OBJECTIVE: Assessing the quality of life and the clinical and social-demographic factors associated in schizophrenic spectrum patients (ICD-10 F20-F29) attending CAPS at the programmatic area 3.0. METHODS: A cross-sectional study was carried out in a sample of schizophrenic spectrum patients who have been enrolled in 2008 in CAPS in programmatic area (AP) 3 at Rio de Janeiro city, using MINIPLUS to assess schizophrenia spectrum disorder and use of psychoactive substances, Positive and Negative Symptoms Scale (PANSS) to assess psychiatric symptoms and Quality of Life Scale (QLS-BR) to assess the quality of life. RESULTS: Seventy nine patients were included, of whom 74 (93.7%) presented some impairment in quality of life. The most frequently affected area was occupational performance. Variables that showed a significant association with severe impairment of quality of life were: marital status, race, occupation, who patients lived with, homelessness, having children, previous psychiatric hospitalization, negative symptoms and symptoms designated as not applicable (being characterized by a lack of typical positive and negative symptoms). CONCLUSION: The knowledge of these factors should be crucial to implement health policies and psychosocial rehabilitation programs focused on improving the quality of life of these patients.

A bio-inspired computational model for motion detection

Tese de Doutoramento (Programa Doutoral em Engenharia Biomédica)