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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(More)
Multidisciplinary team (MDT) meetings are becoming the model of care for cancer patients worldwide. While MDTs have improved the quality of cancer care, the meetings impose substantial time pressure on the members, who generally attend several such MDTs. We describe Lung Cancer Assistant (LCA), a clinical decision support (CDS) prototype designed to assist(More)
This paper describes the modelling of the LUCADA lung cancer ontology in OWL 2 and how this ontology is utilised by the online clinical decision support application Lung Cancer Assistant (LCA) for categorising patients and producing guidelinebased treatment recommendations with the help of ontological inference. LCA is aimed to assist clinicians by(More)
This paper evaluates the performances of the OWL 2 reasoners HermiT, FaCT++ and Pellet in the context of an ontological clinical decision support system in lung cancer care. In the first set of experiments, we compare how the classification and realisation times of the LUCADA and LUCADA-SNOMED CT ontologies vary as we expand their TBoxes with additional(More)
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