Development of a Bayesian Network for the prognosis of head injuries using graphical model selection techniques.
The assessment of a head-injured patient's prognosis is a task that involves the evaluation of diverse sources of information. In this study we propose an analytical approach, using a Bayesian Network (BN), of combining the available evidence. The BN's structure and parameters are derived by learning techniques applied to a database (600 records) of seven clinical and laboratory findings. The BN produces quantitative estimations of the prognosis after 24 hours for head-injured patients in the outpatients department. Alternative models are compared and their performance is tested against the success rate of an expert neurosurgeon.
Sakellaropoulos
G C
GC
Computer Laboratory, School of Medicine, University of Patras, Greece. gsak@med.upatras.gr
Nikiforidis
G C
GC
D001499:Bayes Theorem; D006259:Craniocerebral Trauma / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005103:Expert Systems; D006115:Greece; D006801:Humans; D008959:Models, Neurological; D011379:Prognosis