Decision support system to detect hidden pathologies of stroke: the CIPHER project

Currently, it is difficult to find platforms connected to health systems that exploit data in a coherent way and that allow, on the one hand, to send sanitary warnings and on the other, to validate the performance of medical specialists according to the models set by the best practices of the specialty. This chapter aims to explain the CIPHER project, a decision support system (DSS), based on machine-learning (ML) and big data technologies, capable of alerting a clinician when a situation of risk is detected in a patient suffering from a certain pathology, so that could be able to carry out the appropriate measures. CIPHER, is a project born from scratch. For its development, different methodologies, such as design sprint (for product prototyping), navigational development techniques (for product analysis and testing) or SCRUM (for product development), have been applied. In addition, this product has been defined in direct contact with medical specialists and under the umbrella of international standards and models such as ISO 13606, SNOMED, REGICOR or CHADS2. As a result of the development of this product, we have obtained a DSS, which offers health professionals the possibility of receiving alerts from patientswhomay be at risk of suffering from a specific pathology, based on a series of criteria defined by international standards. Moreover, health professionals would be able to find hidden symptomatology of the pathology mentioned above, which, a priori, are not known.