Intracranial aneurysms (ICAs) are an increasingly common finding, both from incidental discovery on imaging studies and on autopsy; it is estimated that anywhere from 1-6% of the American population will develop this problem. Unfortunately, while our ability to detect ICAs has grown, our fundamental understanding of this disease entity remains lacking and significant debate continues in regards to its treatment. Given the high degree of mortality and comorbidity associated with ruptured intracranial aneurysms, it is imperative that new insights and approaches be developed to inform medical decision making involving ICAs.

The objective of this proposal was the creation of an informatics infrastructure to help elucidate the genesis, progression, and treatment of intracranial aneurysms. A set of technical developments is outlined to transform the array of information routinely collected from clinical assessment of ICA patients into a Bayesian belief network (BBN) that models the disease.