Unifying acute stroke treatment guidelines for a Bayesian belief network.
With the large number of clinical practice guidelines available, there is an increasing need for a comprehensive unified model for acute ischemic stroke treatment to assist in clinical decision making. We present a unified treatment model derived through review of existing clinical practice guidelines, meta-analyses, and clinical trials. Using logic from the treatment model, a Bayesian belief network was defined and fitted to data from our institution's observational quality improvement database for acute stroke patients. The resulting network validates known relationships between variables, treatment decisions and outcomes, and enables the exploration of new correlative relationships not defined in current guidelines.
Medical Imaging Informatics Group, University of California Los Angeles, Los Angeles, CA, USA.
Alexa
A
Love
Corey W
CW
Arnold
Suzie
S
El-Saden
David S
DS
Liebeskind
Lewellyn
L
Andrada
Jeffrey
J
Saver
Alex A T
AA
Bui
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D020000:Decision Support Systems, Clinical / Q000592:standards*; D006801:Humans; D009323:Natural Language Processing; D009462:Neurology / Q000592:standards*; D017063:Outcome Assessment, Health Care / Q000592:standards*; D010363:Pattern Recognition, Automated / Q000379:methods*; D017410:Practice Guidelines as Topic; D020521:Stroke / Q000628:therapy*