Lung cancer assistant: a hybrid clinical decision support application in lung cancer treatment selection

Thesis

We describe an online clinical decision support (CDS) system, Lung Cancer Assistant (LCA), which we have developed to aid the clinicians in arriving at informed treatment decisions for lung cancer patients at multidisciplinary team (MDT) meetings. LCA integrates rule-based and probabilistic decision support within a single platform. To our knowledge, this is the first time this has been achieved in the context of CDS in cancer care.

Rule-based decision support is achieved by an original ontological guideline rule inference framework that operates on a domain-specific module of Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT), containing clinical concepts and guideline rule knowledge elicited from the major national and international guideline publishers. It adopts a conventional argumentation-based decision model, whereby the decision options are listed along with arguments derived by matching the patient records to the guideline rule base. As an additional feature of this framework, when a new patient is entered, LCA displays the most similar patients to the one being viewed.

Probabilistic inference is provided by a Bayesian Network (BN) whose structure and parameters have been learned based on the English Lung Cancer Database (LUCADA). This allows LCA to predict the probability of patient survival and lay out how the selection of different treatment plans would affect it.

Based on a retrospective patient subset from LUCADA, we present empirical results on the treatment recommendations provided by both functionalities of LCA and discuss their strengths and weaknesses. Finally, we present preliminary work, which may allow utilising the BN to calculate survival odd ratios that could be translated into quantitative degrees of support for the guideline rule-based arguments. An online version of LCA is accessible on http://lca.eng.ox.ac.uk.

Authors: Sesen, M

• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: Bayesian Networks; Ontologies
• Subjects: Clinical Decision Support; Lung Cancer