title: Bayesian networks for clinical decision support in lung cancer care.
abstract: Survival prediction and treatment selection in lung cancer care are characterised by high levels of uncertainty. Bayesian Networks (BNs), which naturally reason with uncertain domain knowledge, can be applied to aid lung cancer experts by providing personalised survival estimates and treatment selection recommendations. Based on the English Lung Cancer Database (LUCADA), we evaluate the feasibility of BNs for these two tasks, while comparing the performances of various causal discovery approaches to uncover the most feasible network structure from expert knowledge and data. We show first that the BN structure elicited from clinicians achieves a disappointing area under the ROC curve of 0.75 (± 0.03), whereas a structure learned by the CAMML hybrid causal discovery algorithm, which adheres with the temporal restrictions, achieves 0.81 (± 0.03). Second, our causal intervention results reveal that BN treatment recommendations, based on prescribing the treatment plan that maximises survival, can only predict the recorded treatment plan 29% of the time. However, this percentage rises to 76% when partial matches are included.
authors: Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
authors: M Berkan
authors: MB
authors: Sesen
authors: Ann E
authors: AE
authors: Nicholson
authors: Rene
authors: R
authors: Banares-Alcantara
authors: Timor
authors: T
authors: Kadir
authors: Michael
authors: M
authors: Brady
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001185:Artificial Intelligence; D001499:Bayes Theorem; D016000:Cluster Analysis; D016208:Databases, Factual; D003661:Decision Support Techniques; D003695:Delivery of Health Care; D006801:Humans; D008175:Lung Neoplasms / Q000401:mortality / Q000473:pathology / Q000628:therapy; D009367:Neoplasm Staging; D011379:Prognosis; D015203:Reproducibility of Results