Past attempts to apply Artificial Intelligence (AI) to medical decision support systems have traditionally encountered a strong limitation in the complexity of human language [8]. Today, the state of the art of Natural KeywordsNatural Language Processing, Artificial Intelligence, E-Health, Machine Learning, Electronic Health Records. AbstractHealth information grows exponentially (doubling every 5 years), thus generating a sort of inflation of science, i.e. the generation of more knowledge than we can leverage. In an unprecedented data-driven shift, today doctors have no longer time to keep updated. This fact explains why only one in every five medical decisions is based strictly on evidence, which inevitably leads to variability. A good solution lies on clinical decision support systems, based on big data analysis. As the processing of large amounts of information gains relevance, automatic approaches become increasingly capable to see and correlate information further and better than the human mind can. In this context, healthcare professionals are increasingly counting on a new set of tools in order to deal with the growing information that becomes available to them on a daily basis. By allowing the grouping of collective knowledge and prioritizing "mindlines" against "guidelines", these support systems are among the most promising applications of big data in health. In this demo paper we introduce Savana, an AI-enabled system based on Natural Language Processing (NLP) and Neural Networks, capable of, for instance, the automatic expansion of medical terminologies, thus enabling the re-use of information expressed in natural language in clinical reports. This automatized and precise digital extraction allows the generation of a real time information engine, which is currently being deployed in healthcare institutions, as well as clinical research and management.

The paper highlights the need for an Active System Management (ASM) of distribution networks as a key tool for the efficient and secure integration of a high share of Distributed Energy Resources (DER). The paper provides technical and regulatory recommendations that mainly focus on distributed generation but are also largely applicable to flexible loads, electric vehicles and storage.