title: From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support.
abstract: OBJECTIVE
  (1) To develop a rigorous and repeatable method for building effective Bayesian network (BN) models for medical decision support from complex, unstructured and incomplete patient questionnaires and interviews that inevitably contain examples of repetitive, redundant and contradictory responses; (2) To exploit expert knowledge in the BN development since further data acquisition is usually not possible; (3) To ensure the BN model can be used for interventional analysis; (4) To demonstrate why using data alone to learn the model structure and parameters is often unsatisfactory even when extensive data is available.
  
  
  METHODS
  The method is based on applying a range of recent BN developments targeted at helping experts build BNs given limited data. While most of the components of the method are based on established work, its novelty is that it provides a rigorous consolidated and generalised framework that addresses the whole life-cycle of BN model development. The method is based on two original and recent validated BN models in forensic psychiatry, known as DSVM-MSS and DSVM-P.
  
  
  RESULTS
  When employed with the same datasets, the DSVM-MSS demonstrated competitive to superior predictive performance (AUC scores 0.708 and 0.797) against the state-of-the-art (AUC scores ranging from 0.527 to 0.705), and the DSVM-P demonstrated superior predictive performance (cross-validated AUC score of 0.78) against the state-of-the-art (AUC scores ranging from 0.665 to 0.717). More importantly, the resulting models go beyond improving predictive accuracy and into usefulness for risk management purposes through intervention, and enhanced decision support in terms of answering complex clinical questions that are based on unobserved evidence.
  
  
  CONCLUSIONS
  This development process is applicable to any application domain which involves large-scale decision analysis based on such complex information, rather than based on data with hard facts, and in conjunction with the incorporation of expert knowledge for decision support via intervention. The novelty extends to challenging the decision scientists to reason about building models based on what information is really required for inference, rather than based on what data is available and hence, forces decision scientists to use available data in a much smarter way.
authors.affiliation: Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Campus, Mile End Road, Computer Science Building, E1 4NS London, UK. Electronic address: anthony@constantinou.info.
authors.forename: Anthony Costa
authors.initials: AC
authors.lastname: Constantinou
authors.affiliation: Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Campus, Mile End Road, Computer Science Building, E1 4NS London, UK.
authors.forename: Norman
authors.initials: N
authors.lastname: Fenton
authors.affiliation: Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Campus, Mile End Road, Computer Science Building, E1 4NS London, UK.
authors.forename: William
authors.initials: W
authors.lastname: Marsh
authors.affiliation: Department of Software Engineering, West Pomeranian University of Technology, Szczecin ul. Żołnierska 52, 71-210 Szczecin, Poland.
authors.forename: Lukasz
authors.initials: L
authors.lastname: Radlinski
mesh_terms: D001499:Bayes Theorem; D020000:Decision Support Systems, Clinical; D006801:Humans; D007407:Interviews as Topic; D011795:Surveys and Questionnaires
keywords: Bayesian networks; Belief networks; Causal intervention; Criminology; Decision support; Expert knowledge; Forensic psychiatry; Mental health; Questionnaire data; Survey data
references.citation: J Clin Epidemiol. 2010 Apr;63(4):370-83
references.pmid: 19926253
references.citation: J Biomed Inform. 2014 Apr;48:28-37
references.pmid: 24189161
references.citation: Artif Intell Med. 2016 Jan;66:41-52
references.pmid: 26395654
references.citation: Artif Intell Med. 2000 Jul;19(3):251-79
references.pmid: 10906615
references.citation: Artif Intell Med. 2002 Jun;25(2):123-48
references.pmid: 12031603
references.citation: Radiology. 1982 Apr;143(1):29-36
references.pmid: 7063747
references.citation: Schizophr Bull. 1987;13(2):261-76
references.pmid: 3616518
references.citation: Methods Inf Med. 1992 Jun;31(2):106-16
references.pmid: 1635462
references.citation: Methods Inf Med. 1992 Jun;31(2):90-105
references.pmid: 1635470
references.citation: Artif Intell Med. 1997 May;10(1):59-73
references.pmid: 9177816
references.citation: Artif Intell Med. 1999 Feb;15(2):121-34
references.pmid: 10082177
references.citation: Psychosom Med. 2006 May-Jun;68(3):427-36
references.pmid: 16738075
references.citation: IEEE Trans Syst Man Cybern B Cybern. 2007 Aug;37(4):952-65
references.pmid: 17702292
references.citation: J Biomed Inform. 2008 Feb;41(1):1-14
references.pmid: 17625974