The Framingham study and treatment guidelines for stroke prevention.

In recent years, institutional bodies and scientific societies of principal Western countries have produced several guidelines dealing with risk assessment, primary prevention, and treatment of acute stroke. From a prospective, community-based, observational cohort of patients from the Framingham Heart Study, an absolute estimate of risk for stroke alone or stroke or death was determined based on several risk factors, including advanced age, female sex, increased systolic blood pressure, prior stroke or transient ischemic attack, and diabetes mellitus. This algorithm considers many variables and expresses their results as the percentage of risk of developing a fatal or nonfatal stroke in the following 5 years. The author has identified three major pitfalls of this algorithm, which are related to the limitation of the classic statistical approach in handling this kind of nonlinear and complex information: 1) the very large confidence interval of individual risk assessment, 2) the inability to capture the process dynamics, and 3) the inability to capture the disease complexity. The artificial intelligence armamentarium may provide an advantage in the attempt to overcome these limitations. The theoretic background and some application examples related to artificial neural networks (ANNs) and fuzzy logic are reviewed and discussed. Newer approaches linked to artificial intelligence, such as fuzzy logic and ANNs, seem better at addressing the challenge of the increasing complexity of the predisposing factors linked to cerebrovascular events and at predicting future events in an individual patient.