Bayesian networks for clinical decision support in lung cancer care.

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 selec...

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Autores principales:	M Berkan Sesen, Ann E Nicholson, Rene Banares-Alcantara, Timor Kadir, Michael Brady
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4517b4944c0e40cfaf8de27b476ad65a
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id	oai:doaj.org-article:4517b4944c0e40cfaf8de27b476ad65a
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spelling	oai:doaj.org-article:4517b4944c0e40cfaf8de27b476ad65a2021-11-18T08:43:07ZBayesian networks for clinical decision support in lung cancer care.1932-620310.1371/journal.pone.0082349https://doaj.org/article/4517b4944c0e40cfaf8de27b476ad65a2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24324773/?tool=EBIhttps://doaj.org/toc/1932-6203Survival 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.M Berkan SesenAnn E NicholsonRene Banares-AlcantaraTimor KadirMichael BradyPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 12, p e82349 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q M Berkan Sesen Ann E Nicholson Rene Banares-Alcantara Timor Kadir Michael Brady Bayesian networks for clinical decision support in lung cancer care.
description	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.
format	article
author	M Berkan Sesen Ann E Nicholson Rene Banares-Alcantara Timor Kadir Michael Brady
author_facet	M Berkan Sesen Ann E Nicholson Rene Banares-Alcantara Timor Kadir Michael Brady
author_sort	M Berkan Sesen
title	Bayesian networks for clinical decision support in lung cancer care.
title_short	Bayesian networks for clinical decision support in lung cancer care.
title_full	Bayesian networks for clinical decision support in lung cancer care.
title_fullStr	Bayesian networks for clinical decision support in lung cancer care.
title_full_unstemmed	Bayesian networks for clinical decision support in lung cancer care.
title_sort	bayesian networks for clinical decision support in lung cancer care.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/4517b4944c0e40cfaf8de27b476ad65a
work_keys_str_mv	AT mberkansesen bayesiannetworksforclinicaldecisionsupportinlungcancercare AT annenicholson bayesiannetworksforclinicaldecisionsupportinlungcancercare AT renebanaresalcantara bayesiannetworksforclinicaldecisionsupportinlungcancercare AT timorkadir bayesiannetworksforclinicaldecisionsupportinlungcancercare AT michaelbrady bayesiannetworksforclinicaldecisionsupportinlungcancercare
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Bayesian networks for clinical decision support in lung cancer care.

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