title: Validation workflow for a clinical Bayesian network model in multidisciplinary decision making in head and neck oncology treatment.
abstract: OBJECTIVE
  Oncological treatment is being increasingly complex, and therefore, decision making in multidisciplinary teams is becoming the key activity in the clinical pathways. The increased complexity is related to the number and variability of possible treatment decisions that may be relevant to a patient. In this paper, we describe validation of a multidisciplinary cancer treatment decision in the clinical domain of head and neck oncology.
  
  
  METHODS
  Probabilistic graphical models and corresponding inference algorithms, in the form of Bayesian networks, can support complex decision-making processes by providing a mathematically reproducible and transparent advice. The quality of BN-based advice depends on the quality of the model. Therefore, it is vital to validate the model before it is applied in practice.
  
  
  RESULTS
  For an example BN subnetwork of laryngeal cancer with 303 variables, we evaluated 66 patient records. To validate the model on this dataset, a validation workflow was applied in combination with quantitative and qualitative analyses. In the subsequent analyses, we observed four sources of imprecise predictions: incorrect data, incomplete patient data, outvoting relevant observations, and incorrect model. Finally, the four problems were solved by modifying the data and the model.
  
  
  CONCLUSIONS
  The presented validation effort is related to the model complexity. For simpler models, the validation workflow is the same, although it may require fewer validation methods. The validation success is related to the model's well-founded knowledge base. The remaining laryngeal cancer model may disclose additional sources of imprecise predictions.
authors.affiliation: Innovation Center Computer Assisted Surgery, University of Leipzig, Semmelweisstr. 14, 04103, Leipzig, Germany. mario.cypko@medizin.uni-leipzig.de.
authors.forename: Mario A
authors.initials: MA
authors.lastname: Cypko
authors.affiliation: Clinic of Otolaryngology, Head and Neck Surgery, Department of Head Medicine and Oral Health, University of Leipzig, Leipzig, Germany.
authors.forename: Matthaeus
authors.initials: M
authors.lastname: Stoehr
authors.affiliation: School of Information Sciences, University of Pittsburgh, Pittsburgh, PA, USA.|Faculty of Computer Science Bialystok University of Technology, Bialystok, Poland.
authors.forename: Marcin
authors.initials: M
authors.lastname: Kozniewski
authors.affiliation: School of Information Sciences, University of Pittsburgh, Pittsburgh, PA, USA.|Faculty of Computer Science Bialystok University of Technology, Bialystok, Poland.
authors.forename: Marek J
authors.initials: MJ
authors.lastname: Druzdzel
authors.affiliation: Clinic of Otolaryngology, Head and Neck Surgery, Department of Head Medicine and Oral Health, University of Leipzig, Leipzig, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Dietz
authors.affiliation: New York Methodist Hospital, Brooklyn, NY, USA.
authors.forename: Leonard
authors.initials: L
authors.lastname: Berliner
authors.affiliation: Innovation Center Computer Assisted Surgery, University of Leipzig, Semmelweisstr. 14, 04103, Leipzig, Germany.
authors.forename: Heinz U
authors.initials: HU
authors.lastname: Lemke
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D000066491:Clinical Decision-Making; D003657:Decision Making; D006801:Humans; D007822:Laryngeal Neoplasms / Q000473:pathology / Q000628:therapy*; D015233:Models, Statistical; D009367:Neoplasm Staging; D015203:Reproducibility of Results; D057188:Workflow
keywords: Bayesian network; Head and neck oncology; Laryngeal cancer; Model validation; Multidisciplinary tumor board; Therapy decision support system