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

G. C. Sakellaropoulos; G. C. Nikiforidis

doi:10.1055/s-0038-1634146

Methods of Information in Medicine, Table of Contents

Methods Inf Med 1999; 38(01): 37-42
DOI: 10.1055/s-0038-1634146

Original article

Schattauer GmbH

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

G. C. Sakellaropoulos

¹Computer Laboratory, School of Medicine, University of Patras, Greece

,

G. C. Nikiforidis

¹Computer Laboratory, School of Medicine, University of Patras, Greece

› Author Affiliations

Abstract

Abstract:

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.

Keywords:

Bayesian Networks - Head Injuries - Prognosis - Learning Models

Full Text

References

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