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[ES] La asignatura Sistemas de Información y Telemedicina I del Grado en Ingeniería Biomédica de la Universitat Politècnica de València ofrece a los alumnos competencias en el ámbito de la informática aplicada a la medicina. Entre sus resultados de aprendizaje encontramos el diseñar y gestionar un sistema de información hospitalaria. Estos resultados requieren definir e integrar múltiples componentes software especializados en distintos dominios médicos, con una compleja interacción entre ellos, y con variabilidad asociada a las características de cada hospital. Presentamos una nueva propuesta de aprendizaje activo mediante juego de roles en la que los alumnos, representando a jefes de distintos servicios de un hospital y compañías de software, negocian la adquisición de un nuevo componente de sistemas de información en función de las necesidades de su departamento y globales al hospital, interiorizando el diseño del mismo e interacciones entre componentes, y trabajando competencias transversales como trabajo en equipo y liderazgo y comunicación efectiva. Hacia una mejora continua, recogemos mediante cuestionarios on-line retroalimentación de los alumnos sobre la actividad. Aplicada desde el curso 2018-19, la actividad ha demostrado su efectividad, cumpliendo los resultados de aprendizaje, con una buena apreciación del aprendizaje recibido por los alumnos, y una elevada tasa de retroalimentación., [EN] The Health Information Systems and Telemedicine I course of the Degree in Biomedical Engineering of the Universitat Politècnica de València, Spain, offers students competences in the field of computer science applied to medicine. Among its learning outcomes is the design and management of a hospital information system. These learning outcomes require the definition and integration of multiple software components specialized in different medical and management domains, with a complex interaction between them, and with variability associated with the characteristics of each hospital. Active learning through role-playing is a learning strategy that is well suited for learning these types of competences. We present a new proposal for active learning through role-playing in which students, representing heads of the different services of a hospital and software companies, negotiate the acquisition of a new component of information systems according to the needs of their department and global needs to the hospital, internalizing the design of components and their interactions, while working transversal competences such as teamwork and leadership and effective communication, among others. Additionally, towards a continuous improvement, after the session we collect through voluntary on-line questionnaires the students' opinions about the activity and about the perceived learning. Applied since the 2018-19 academic year, the activity has demonstrated its effectiveness, accomplishing the pedagogical objectives of the subject, with a good appreciation of the learning received by the students, and presenting a high rate of feedback.

The evidence that quality of life is a positive variable for the survival of cancer patients has prompted the interest of the health and pharmaceutical industry in considering that variable as a final clinical outcome. Sustained improvements in cancer care in recent years have resulted in increased numbers of people living with and beyond cancer, with increased attention being placed on improving quality of life for those individuals. Connected Health provides the foundations for the transformation of cancer care into a patient-centric model, focused on providing fully connected, personalized support and therapy for the unique needs of each patient. Connected Health creates an opportunity to overcome barriers to health care support among patients diagnosed with chronic conditions. This paper provides an overview of important areas for the foundations of the creation of a new Connected Health paradigm in cancer care. Here we discuss the capabilities of mobile and wearable technologies; we also discuss pervasive and persuasive strategies and device systems to provide multidisciplinary and inclusive approaches for cancer patients for mental well-being, physical activity promotion, and rehabilitation. Several examples already show that there is enthusiasm in strengthening the possibilities offered by Connected Health in persuasive and pervasive technology in cancer care. Developments harnessing the Internet of Things, personalization, patient-centered design, and artificial intelligence help to monitor and assess the health status of cancer patients. Furthermore, this paper analyses the data infrastructure ecosystem for Connected Health and its semantic interoperability with the Connected Health economy ecosystem and its associated barriers. Interoperability is essential when developing Connected Health solutions that integrate with health systems and electronic health records. Given the exponential business growth of the Connected Health economy, there is an urgent nee

Actigraphy devices have been successfully used as effective tools in the treatment of diseases such as sleep disorders or major depression. Although several efforts have been made in recent years to develop smaller and more portable devices, the features necessary for the continuous monitoring of outpatients require a less intrusive, obstructive and stigmatizing acquisition system. A useful strategy to overcome these limitations is based on adapting the monitoring system to the patient lifestyle and behavior by providing sets of different sensors that can be worn simultaneously or alternatively. This strategy offers to the patient the option of using one device or other according to his/her particular preferences. However this strategy requires a robust multi-sensor fusion methodology capable of taking maximum profit from all of the recorded information. With this aim, this study proposes two actigraphy fusion models including centralized and distributed architectures based on artificial neural networks. These novel fusion methods were tested both on synthetic datasets and real datasets, providing a parametric characterization of the models' behavior, and yielding results based on real case applications. The results obtained using both proposed fusion models exhibit good performance in terms of robustness to signal degradation, as well as a good behavior in terms of the dependence of signal quality on the number of signals fused. The distributed and centralized fusion methods reduce the mean averaged error of the original signals to 44% and 46% respectively when using simulated datasets. The proposed methods may therefore facilitate a less intrusive and more dependable way of acquiring valuable monitoring information from outpatients., Peer Reviewed, Postprint (published version)

[EN] H-1 MRS is becoming an accurate, non-invasive technique for initial examination of brain masses. We investigated if the combination of single-voxel H-1 MRS at 1.5 T at two different (TEs), short TE (PRESS or STEAM, 20-32 ms) and long TE (PRESS, 135-136 ms), improves the classification of brain tumors over using only one echo TE. A clinically validated dataset of 50 low-grade meningiomas, 105 aggressive tumors (glioblastoma and metastasis). and 30 low-grade glial tumors (astrocytomas grade II, oligodendrogliomas anti oligoastrocytomas) was used to fit predictive models based on the combination of features from short-TEs and long-TE spectra. A new approach that combines the two consecutively was used to produce a single data vector from which relevant features of the two TE spectra could be extracted by means of three algorithms: stepwise, reliefF, and principal components analysis. Least Squares support vector machines and linear discriminant analysis were applied to fit the pairwise and multiclass classifiers, respectively. Significant differences in performance were found when short-TE, long-TE or both spectra combined were used as input. In our dataset, to discriminate meningiomas, the combination of the two TE acquisitions produced optimal performance. To discriminate aggressive tumors from low-grade filial tumours, the use of short-TE acquisition alone was preferable. The classifier development strategy used here lends itself to automated learning and test performance processes, which may, be of use for future web-based multicentric classifier development studies. Copyright (c) 2008 John Wiley & Sons, Ltd.