title: Incorporating expert knowledge when learning Bayesian network structure: a medical case study.
abstract: OBJECTIVE
  Bayesian networks (BNs) are rapidly becoming a leading technology in applied Artificial Intelligence, with many applications in medicine. Both automated learning of BNs and expert elicitation have been used to build these networks, but the potentially more useful combination of these two methods remains underexplored. In this paper we examine a number of approaches to their combination when learning structure and present new techniques for assessing their results.
  
  
  METHODS
  Using public-domain medical data, we run an automated causal discovery system, CaMML, which allows the incorporation of multiple kinds of prior expert knowledge into its search, to test and compare unbiased discovery with discovery biased with different kinds of expert opinion. We use adjacency matrices enhanced with numerical and colour labels to assist with the interpretation of the results. We present an algorithm for generating a single BN from a set of learned BNs that incorporates user preferences regarding complexity vs completeness. These techniques are presented as part of the first detailed workflow for hybrid structure learning within the broader knowledge engineering process.
  
  
  RESULTS
  The detailed knowledge engineering workflow is shown to be useful for structuring a complex iterative BN development process. The adjacency matrices make it clear that for our medical case study using the IOWA dataset, the simplest kind of prior information (partially sorting variables into tiers) was more effective in aiding model discovery than either using no prior information or using more sophisticated and detailed expert priors. The method for generating a single BN captures relationships that would be overlooked by other approaches in the literature.
  
  
  CONCLUSIONS
  Hybrid causal learning of BNs is an important emerging technology. We present methods for incorporating it into the knowledge engineering process, including visualisation and analysis of the learned networks.
authors.affiliation: Departamento de Sistemas Inforḿaticos SIMD i(3)A, Universidad de Castilla-La Mancha, Campus Universitario s/n, Spain.
authors.forename: M
authors.initials: M
authors.lastname: Julia Flores
authors.forename: Ann E
authors.initials: AE
authors.lastname: Nicholson
authors.forename: Andrew
authors.initials: A
authors.lastname: Brunskill
authors.forename: Kevin B
authors.initials: KB
authors.lastname: Korb
authors.forename: Steven
authors.initials: S
authors.lastname: Mascaro
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D020000:Decision Support Systems, Clinical; D005103:Expert Systems; D006333:Heart Failure / Q000175:diagnosis*; D006801:Humans; D051188:Knowledge Bases; D011237:Predictive Value of Tests; D011379:Prognosis; D018511:Systems Integration