Knowledge-based system for the diagnosis and treatment of hypertension

A knowledge-based system to assist the physician in the diagnosis and treatment of hypertension has been developed as the result of cooperation between the Department of Electronic Engineering of the University of Florence and the Interuniversity Centre of Clinical Chronobiology. The system input consists of the data recorded over a 24 h (or longer) period by monitoring (automatically or through self-measurements) the blood pressure of the subject undergoing the system analysis, and the associatd anamnestic data. The process results in a report that states from which kind of hypertensive syndrome, if any, the subject is suffering and which anti-hypertensive therapy appears to be most suitable. The system consists of three modules: the first diagnoses hypertension by applying cluster analysis to a set of parameters derived from the principal components of the time series resulting from the subject's blood pressure monitoring; the other two classify hypertension and offer advice about the most advisable treatment, respectively, by using high-level data representation and processing. The knowledge embedded in the system is internally represented by means of frames and rules. This paper describes the structure of the system, illustrates the techniques that have been used for its development and discusses the results of its application. A knowledge-based system to assist the physician in the diagnosis and treatment of hypertension has been developed as the result of cooperation between the Department of Electronic Engineering of the University of Florence and the Interuniversity Centre of Clinical Chronobiology. The system input consists of the data recorded over a 24 h (or longer) period by monitoring (automatically or through self-measurements) the blood pressure of the subject undergoing the system analysis, and the associated anamnestic data. The process results in a report that states from which kind of hypertensive syndrome, if any, the subject is suffering and which anti-hypertensive therapy appears to be most suitable. The system consists of three modules: the first diagnoses hypertension by applying cluster analysis to a set of parameters derived from the principal components of the time series resulting from the subject's blood pressure monitoring; the other two classify hypertension and offer advice about the most advisable treatment, respectively, by using high-level data representation and processing. The knowledge embedded in the system is internally represented by means of frames and rules. This paper describes the structure of the system, illustrates the techniques that have been used for its development and discusses the results of its application.