MED-P aims to improve the diagnosis, treatment and research in chronic, oncological, degenerative and rare diseases, and in first contact with the system, whether for emergencies or flow management; through personalized medicine with new BigData tools._x000D_ The Health Services will be provided with instruments that allow them to make health decisions adapted to the individual characteristics and needs of each patient. For this, we will consider clinical, pathological, image, prognostic and predictive variables; as well as other non-clinical and available outside the health system (lifestyle, adherence to treatment, etc.)_x000D_ The solution/s will integrate data from various sources, and will facilitate their analysis and exploitation, allowing to determine patterns of disease evolution, therapeutic response, etc. It should be focused on the management of these diseases with criteria of opportunity, effectiveness, efficiency, feasibility and sustainability of health care._x000D_ To reach the final result of generating useful knowledge in that episode, for the physician (SSDC) or the machine with patient interface (virtual assistant to the patient for diagnosis, simple treatment, extension of reports and promotion of personalized health in an independent project); there will be:_x000D_ Management and Treatment of large volumes of Data._x000D_ Analytics (textmining, datamining, statistical, predictive and descriptive models)._x000D_ Open Data._x000D_ Clinical quality assessment of D._x000D_ Search engines._x000D_ Interoperability and security of big data services in health and research repositories._x000D_ Machine learning "Machine Learning" and predictive models._x000D_ Prognoses through similarity of clinical routes._x000D_ Aggregate medical evidence using decision theory._x000D_ Deployment of multiscale and multilevel patient modeling._x000D_ Multi-criteria stratification of patients._x000D_ Early and quantitative evaluation of treatment._x000D_ Interrogation to repositories through abstractions.