## 3 Challenges to be Overcome in an ICT--Based Diabetes Management System
It is time to aim for a complete management system and, with the aim of synthesizing the previous considerations; it is possible to summarize the above in terms of the following challenges:  
* Full sensor integration. Technological advances have introduced new sensors, some of them specifically designed for diabetes care and others conceived for health information generally. These devices use different means of transmitting information. Therefore, it is necessary to create a platform that is able to deal with all of them successfully. * Novel ways to deal with information, generating new variables. The need to include the \"on board\" concept in other features has been studied. Moreover, it is necessary to generate, in a consistent way, a definitive features set that differentiates the importance and relevance of the different variables. * Pattern identification/prediction of glycemia. Being able to identify patterns enables us to make predictions. In this sense, glycemia prediction is the most interesting ability that is mentioned in this work. A reliable forecast allows for the anticipation of risky situations, such as hypoglycemia or maintained hyperglycemia. In this sense, the application of Machine Learning techniques is continually growing. Artificial Neural Networks offer an adaptive and flexible way to predict glycemia or give some advice about the insulin dosage [12]. * Optimized solutions. With the previous step solved, it is possible to offer a suggestion about insulin dosage or health tips. Global diabetes management will advise about insulin injections (with the proper dosage), which will always require the user's approval, thus avoiding the misfortune of wrong decisions. Going further, the platform needs to be able to identify suboptimal situations, for instance, a sedentary lifestyle or lack of rest. Using a cloud computing resource, the complexity of the computerized models and the optimization process can be overcome without limitations. * Management of a big amount of data. CGM usually provides data with a periodicity that ranges from 1 to 5 min. Other sensors previously mentioned, for instance, a heart-rate monitor, should also capture data in the range of minutes. This leads to the generation of a huge amount of data that have been compiled at short intervals. Thus, it is a good decision to introduce Big Data analysis [50; 51]. This is a concept that applies to data sets that are so large or complex that traditional data-processing applications become useless. Some challenges in this field include analysis, capture, search, sharing, storage, transfer, visualization, querying, updating, and information privacy. The term \"Big Data\" often refers simply to the use of predictive analytics or certain other advanced methods in order to extract value from the data. It is easy to find examples of the use of this resource in health care to add useful knowledge to the obtained data [52]. * Emergency control. Under certain circumstances, the diabetes platform needs to take control of the situation. The main dangerous situation is strong hypoglycemia, which can lead to a loss of consciousness, or, in extreme cases, death. To mitigate the consequences of hypoglycemia, it is necessary, firstly, to stop the infusion pump in the case of a diabetic person using the device, for example, and secondly, to warn emergency services and medical staff about the status of the patient. Moreover, the systems will provide a historical record of the last hours, including insulin dosages, meals, and physical activity. * Easy inter-connection between patients, caregivers, and medical workers. In a world that is characterized by information flows, it is mandatory to address valuable knowledge in many directions, contacting all of the subjects involved in the diabetes management.