Abstract 161P
Background
The incidence of lung cancer has been increasing. Healthcare providers are trying to acquire more knowledge of the disease’s biology to treat their patients better. However, the information available is more than humans can comprehend. Predictive models such as Bayesian networks, which can intricately represent causal relations between variables, are suitable structures to model this information. However, the conventional methods for developing Bayesian network structures are limited in healthcare.
Methods
545 NSCLC patients treated primarily with (chemo)radiation therapy from Maastro clinic in the Netherlands between 2010 and 2013 were considered to develop this Bayesian network structure. All continuous variables were binned before learning the structure process. Patients with missing survival status and variables with more than 25% missing information were excluded from the structure-building process. The causal relationship (arcs) between variables in the data was determined using the hill-climbing algorithm with domain experts’ restrictions over bootstrap runs (B=400). The final structure consists of nodes and arcs, where arcs (relationships) are present in at least 70% of all bootstrap samples. Structural performance was assessed by computing the area under the curve (AUC) values and producing calibration plots based on a repeated 5-fold cross-validation. In addition, an adapted pre-specified expert structure was compared with a structure developed from the method in this study.
Results
Tumor load was removed from the final Bayesian network structure due to its high percentage (37%) of missing information. The final cohort used to develop the structure was reduced to 499, excluding 46 (08.4%) patients with missing survival status. The resulting structure’s mean AUC and confidence interval to predict two-year survival was 0.614 (0.499 - 0.730). The performance of the compared structures was only slightly above the chance level, but the structure based on the method in this study was clinically more correct.
Conclusions
The aim of this study was attained and shows that Bayesian network structures which combine expert knowledge with a rigorous structure-learning algorithm produce a clinically valid structure.
Legal entity responsible for the study
Biche Osong.
Funding
Has not received any funding.
Disclosure
All authors have declared no conflicts of interest.