This article describes the design, development and implementation of a set of microservices based on an architecture that enables detection and assisted clinical diagnosis within the field of infectious diseases of elderly patients, via a telemonitoring system. The proposed system is designed to continuously update a medical database fed with vital signs from biosensor kits applied by nurses to elderly people on a daily basis. The database is hosted in the cloud and is managed by a flexible microservices software architecture. The computational paradigms of the edge and the cloud were used in the implementation of a hybrid cloud architecture in order to support versatile high-performance applications under the microservices pattern for the pre-diagnosis of infectious diseases in elderly patients. The results of an analysis of the usability of the equipment, the performance of the architecture and the service concept show that the proposed e-health system is feasible and innovative. The system components are also selected to give a cost-effective implementation for people living in disadvantaged areas. The proposed e-health system is also suitable for distributed computing, big data and NoSQL structures, thus allowing the immediate application of machine learning and AI algorithms to discover knowledge patterns from the overall population.

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Nowadays, the application of methodologies that allow to guide the process of development of Software in the companies has become a fundamental aspect to achieve the suitable management of the processes in the projects. In view of the diversity of existing methodologies, there is a growing interest in defining strategies that allow the selection and application of the correct methodology, which adjusts to the characteristics of the work teams and the software projects. The aim of this work is to develop an ontology for the selection of the methodology that, according to its principles, is most appropriate and beneficial for the development of software projects. The domain of ontology is limited to the Agile and Lean approaches, without defining for Agile the specific method that it applies, but it involves any method that is governed by the agile values and principles established in the Agile manifesto. Onto-logy is applied in two organizations in the banking sector, allowing recommendations to be inferred for the use of Agile methodology in both, which will make it possible to reduce the delivery time of software products, improve communication between project participants, and facilitate the engineering of requirements. On the other hand, the ontology suggests co-regulating characteristic aspects of the Lean practices in order to minimize costs, optimize processes in the software projects and improve the organizational culture.

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A patient suffering from advanced chronic renal disease undergoes several dialysis sessions on different dates. Several clinical parameters are monitored during the different hours of any of these sessions. These parameters, together with the information provided by other parameters of analytical nature, can be very useful to determine the probability that a patient may suffer from hypotension during the session, which should be specially watched since it represents a proven factor of possible mortality. However, the analytical information is not always available to the healthcare personnel, or it is far in time, so the clinical parameters monitored during the session become key to the prevention of hypotension. This article presents an investigation to predict the appearance of hypotension during a dialysis session, using predictive models trained from a large dialysis database, which contains the clinical information of 98,015 sessions corresponding to 758 patients. The prediction model takes into account up to 22 clinical parameters measured five times during the session, as well as the gender and age of the patient. This model was trained by means of machine learning classifiers, providing a success in the prediction higher than 80%.

The derelict Remance gold mine is a possible source of pollution with potentially toxic elements (PTEs). In the study area, diverse mine waste has been left behind and exposed to weather conditions, and poses risks for soil, plants and water bodies, and also for the health of local inhabitants. This study sought to perform an ecological and health risk assessment of derelict gold mining areas with incomplete remediation, including: (i) characterizing the geochemical distribution of PTEs; (ii) assessing ecological risk by estimating the pollution load index (PLI) and potential ecological risk index (RI); (iii) assessing soil health by dehydrogenase activity; and iv) establishing non-carcinogenic (HI) and carcinogenic risks (CR) for local inhabitants. Soil health seems to depend on not only PTE concentrations, but also on organic matter (OM). Both indexes (PLI and RI) ranged from high to extreme near mining and waste accumulation sites. As indicated by both the HI and CR results, the mining area poses a health risk for local inhabitants and particularly for children. For this reason, it will be necessary to set up environmental management programs in the areas that are most affected (tailings and surrounding areas) and accordingly establish the best remediation strategies to minimize risks for the local population.

Background: treating infectious diseases in elderly individuals is difficult; patient referral to emergency services often occurs, since the elderly tend to arrive at consultations with advanced, serious symptoms. Aim: it was hypothesized that anticipating an infectious disease diagnosis by a few days could significantly improve a patient’s well-being and reduce the burden on emergency health system services. Methods: vital signs from residents were taken daily and transferred to a database in the cloud. Classifiers were used to recognize patterns in the spatial domain process of the collected data. Doctors reported their diagnoses when any disease presented. A flexible microservice architecture provided access and functionality to the system. Results: combining two different domains, health and technology, is not easy, but the results are encouraging. The classifiers reported good results; the system has been well accepted by medical personnel and is proving to be cost-effective and a good solution to service disadvantaged areas. In this context, this research found the importance of certain clinical variables in the identification of infectious diseases. Conclusions: this work explores how to apply mobile communications, cloud services, and machine learning technology, in order to provide efficient tools for medical staff in nursing homes. The scalable architecture can be extended to big data applications that may extract valuable knowledge patterns for medical research.