Abstract: A diagnostic enactive medical system that guides a user during acquisition and analyses of medical data for diagnosis and risk assessment. A method of using data-centric analysis and interpretation of acquired medical data in conjunction with metadata management in the point-of-care enactive medical system transforms raw medical data to generate feature-sets of a small number of closely related features associated with a particular medical or physiological state. Medical data from the point-of-care enactive medical system converges onto one or more feature-sets, interacts with the user to provide commentary or request additional information or data concerning a patient. Using an expert knowledgebase, the point-of-care enactive medical system learns from the medical data and then provides the user of tasks suitable for dynamic construction of point-of-care enactive medical knowledge, diagnoses, and recommendations for risk and/or treatment.

Abstract: A knowledgebase comprising a plurality of feature-sets is created using complexity science. The knowledgebase is accessible through a network to a computer, a system, and a computer program code that receives and analyzes medical data to determine the existence of a disease state. The medical data is input and correlated to features within the knowledgebase to identify feature-sets, each feature-set indicating a particular medical condition. After one or more feature-sets have been selected, associative algorithms consider the magnitudes or values of the medical data and, with the values of features in the feature-sets, assess the risk burden of the medical condition associated with the feature-set. An output is generated that may include diagnoses of one or more medical conditions, the risk burden of the medical condition(s), possible treatment options and prevention techniques.