On Cognitive Management Architectures for Enhancing Septic Shock Predictions in Intensive Care Units

Although today’s advanced biomedical technology provides unsurpassed power in diagnosis, monitoring, and treatment, interpretation of vast streams of information generated by this technology often poses excessive demands on the cognitive skills of health-care personnel (nurses, doctors, etc). These problems are most severe in critical care environments such as Intensive Care Units (ICUs) where many events are life-threatening and thus require immediate attention and the implementation of definitive corrective actions. Septic syndrome is one of the most common pathogens of ICUs. Early diagnosis of this condition is complicated by nonspecific clinical signs and symptoms and the fact that not all infections lead to sepsis and to progression into septic shock. On this basis, the use of Cognitive Clinical Management Systems (CCMSs) is receiving increasing interest in recent years, enabling increased precision of diagnostics and novel clinical decision making solutions. Such systems operate on the basis of collecting information from various sources, intelligently processing it, integrating knowledge and experience, and finally, taking the most appropriate clinical decisions. Following the above, the ambition of the present study is to create a CCMS architecture for direct patient use with real-time monitoring, namely I-CSSP, being able to support intelligent septic shock predictions in ICUs.