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G. C. Sakellaropoulos, G. C. Nikiforidis

Development of a Bayesian Network for the Prognosis of Head Injuries using Graphical Model Selection Techniques

Keywords: Bayesian Networks, Head Injuries, Prognosis, Learning Models

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.

Methods of Information in Medicine, Schattauer

Print ISSN: 0026-1270
Volume: 38, 03/1999
Pages: 37 - 42

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