Prognostic performance of two expert systems based on Bayesian belief networks

A decision support system for the prognosis at 24 h of head-injured patients of the intensive care unit (ICU), based on Bayesian belief networks, is constructed by model selection methods applied to a database (637 cases) of seven clinical and laboratory variables. Its performance is compared to other systems, including a simpler belief network that assumes conditional independence among the findings, and a human expert. Results indicate that its performance is not significantly different than that of the neurosurgeon expert and better than the performance of the independence model. Thus, the prognostic judgment of non-neurosurgeon ICU clinicians can be aided by the use of this system.