title: Support of diagnosis of liver disorders based on a causal Bayesian network model.
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 is 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.
authors.affiliation: Medical Center of Postgraduate Education, Institute of Biocybernetics and Medical Engineering PAS, ul. Marymoncka 99, 01-813 Warsaw. hwasyluk@cmkp.edu.pl
authors.forename: H
authors.initials: H
authors.lastname: Wasyluk
authors.forename: A
authors.initials: A
authors.lastname: Oniśko
authors.forename: M J
authors.initials: MJ
authors.lastname: Druzdzel
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D008107:Liver Diseases / Q000175:diagnosis*; D015203:Reproducibility of Results