• Clinical Applications
• Decision Support System
• Features Extraction

Today the importance of knowledge for medicine as a task requiring computer support in clinical and applied research receives a great emphasis. As pointed out by several researchers, although there is the need of a personalized treatment, nowadays there is a little knowledge about how to identify the most suitable treatment or integration of treatment for each specific patient. There is a need to bring the assessment and the therapy out of the clinical environment and develop a patient-centric home-based intervention solution requiring a minimal human involvement and therefore extremely cost effective. The strict focus on the medical setting has now broadened across the healthcare spectrum, and instead of artificial intelligence systems, it is typical to describe them as Clinical Decision Support Systems (CDSS). Pattern classification, expert and artificial intelligence systems, better described as knowledge-based CDSS, are today found an effective application and represent in perspective a real technological breakthrough. A CDSS consists of the objective and quantitative assessment of clinical data; the decision support for treatment planning through pattern classification algorithms; and the provision of warnings and motivating feedback to improve compliance and long-term outcome. Developments in computational techniques including clinical decision support systems, information processing, wireless communication and data mining hold new premises in personal health systems. Pervasive healthcare architectures are today found an effective application and represent in perspective a real technological breakthrough promoting a paradigm shift from diagnosis and treatment of patients based on symptoms to diagnosis and treatment based on risk assessment. Such architectures must be able to collect and manage a large quantity of data supporting the physicians in their decision process through a continuous pervasive remote monitoring model aimed to enhance the understanding of the dynamic disease state and personal risk. The medical knowledge is frequently updated and re-evaluated comprising new risk factors identification, new drugs and diagnostic tests, new evidences from clinical studies. The challenge faced today is to incorporate the most recent and evidence-based knowledge into personal health systems and to transform collected information into valuable knowledge and intelligence to support decision making. Several expert systems tailored to specific diseases are nowadays available in clinical research, often covering the topics addressed by European priorities. Technology can play a key role to gain the continuity of care and a person-centric model, focusing on a knowledge-based approach integrating past and current data of each patient together with statistical evidences. In currently applied care practices, the emergence of clinical symptoms allows a disease to be discovered. Only then, a diagnosis is obtained and a treatment is provided. Currently, different healthcare practice models are used. In some models, the Hospital is the core of the care and any level of technology available at the patient site may help in providing information useful for both monitoring, early diagnosis and preventive treatments. In other models dedicated call centres or point of care act as an intermediary between hospital/heath care professional and patients. Many of the solutions available today on the market follow the above-mentioned model and call centre services or point of care are used by the patients just as a complement to the hospital-centred healthcare services. In a more advanced concept focused on the empowerment, the ownership of the care service is fully taken by the individual. This model is suitable for any of the stages of an individual’s care cycle, providing prevention, early diagnosis services and personalized chronic disease management. Under this model, the technological innovations can help each persons to self-engage and manage his/her own health status, minimizing any interaction with other health care actors. Solutions fully led by the patients are the overwhelming majority of those developed by research efforts covering chronic disease management, lifestyle management and independent living. Even if, in the clinical practice this model has not been yet implemented, it can be considered as a target to be reached achieving at the same time the empowerment of the users and the reduction of workload and costs, preserving the quality and safety of care. However, this model often fails to give the expected results and research is under development. This happens for a series of concomitant causes, ranging from legal and societal obstacles, to the issues to be tackled before these wearable devices are ready for general use, up to the inappropriate use of the decision support system, as well as to win the scepticism of many healthcare professionals. Wearable devices need to be non-intrusive, easy to use, comfortable to wear, efficient in power consumption, privacy compliant, with very low failure rates and high accuracy in triggering alarms, especially if used for diagnostic purposes. The decision support system must infuse clinical knowledge into methodology and technology, thus enhancing the reliability of high-level processing systems customized to his/her personal needs represents the next critical step. The currently used approaches are only based on values of health-related parameters, often monitored instantaneously during a check-up. Moreover, the correlation across physiological, psycho-emotional, environmental and behavioural parameters are poorly explored, because the diagnosis of a disease largely depends on the experience of the individual doctors.