Support of diagnosis of liver disorders based on a causal Bayesian network model

Authors:

Hanna Wasyluk
The Medical Center of Postgraduate Education
Warsaw, Marymoncka 99, Poland
e-mail: hwasyluk@cmkp.edu.pl

Agnieszka Onisko
Bialystok University of Technology
Institute of Computer Science
Bialystok, 15-351, Poland
e-mail: aonisko@ii.pb.bialystok.pl

Marek J. Druzdzel
Decision Systems Laboratory
School of Information Sciences
and Intelligent Systems Program
University of Pittsburgh
e-mail: marek@sis.pitt.edu

Abstract:

We describe our work on HEPAR II, a probabilistic causal model for diagnosis of liver disorders. The model, a Bayesian network capturing the causal interactions among various risk factors, diseases, symptoms, and test results, is based on expert knowledge combined with clinical data captured in medical records. The main applications of HEPAR II are assistance in diagnosis and training of beginning diagnosticians. We outline the principles of the applied approach, present a brief description of the model, and report its diagnostic performance.

Keywords: Medical decision support systems, Bayesian networks.