Title :
Probabilistic diagnostic reasoning: towards improving diagnostic efficiency
Author :
Provan, Gregory M.
Author_Institution :
Dept. of Comput. & Inf. Sci., Pennsylvania Univ., Philadelphia, PA, USA
Abstract :
The author describes a new approximation method which can significantly improve the computational efficiency of Bayesian networks. He applies this technique to the diagnosis of acute abdominal pain, with good results. This approach is based on using a reduced set of the model parameters for diagnostic reasoning. The tradeoffs in diagnostic accuracy required to obtain increased computational efficiency (due to the smaller models) are carefully specified using a variety of statistical metrics
Keywords :
Bayes methods; inference mechanisms; medical diagnostic computing; statistical analysis; Bayesian networks; acute abdominal pain; approximation method; computational efficiency; diagnostic accuracy; diagnostic efficiency; medical diagnosis; model parameters; probabilistic diagnostic reasoning; statistical metrics; Abdomen; Approximation methods; Bayesian methods; Computational efficiency; Computer networks; Decision making; Information science; Intrusion detection; Pain; Power system modeling;
Conference_Titel :
Artificial Intelligence for Applications, 1994., Proceedings of the Tenth Conference on
Conference_Location :
San Antonia, TX
Print_ISBN :
0-8186-5550-X
DOI :
10.1109/CAIA.1994.323642
