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dc.contributor.authorAfzal, Muhammad
dc.contributor.authorMalik, Khalid M.
dc.contributor.authorAli, Taqdir
dc.contributor.authorAli Khan, Wajahat
dc.contributor.authorIrfan, Muhammad
dc.contributor.authorJamshrf, Arif
dc.contributor.authorLee, Sungyoung
dc.contributor.authorHussain, Maqbool
dc.date.accessioned2020-09-01T12:02:23Z
dc.date.available2020-09-01T12:02:23Z
dc.date.issued2020-08-19
dc.identifier.citationHussain, M., Afzal, M., Malik, K.M., Ali, T., Khan, W.A., Irfan, M., Jamshrf, A. and Lee, S., (2020). 'Acquiring guideline-enabled data driven clinical knowledge model using formally verified refined knowledge acquisition method'. Computer Methods and Programs in Biomedicine, pp. 1-26.en_US
dc.identifier.issn01692607
dc.identifier.doi10.1016/j.cmpb.2020.105701
dc.identifier.urihttp://hdl.handle.net/10545/625145
dc.description.abstractBackground and Objective: Validation and verification are the critical requirements for the knowledge acquisition method of the clinical decision support system (CDSS). After acquiring the medical knowledge from diverse sources, the rigorous validation and formal verification process are required before creating the final knowledge model. Previously, we have proposed a hybrid knowledge acquisition method with the support of a rigorous validation process for acquiring medical knowledge from clinical practice guidelines (CPGs) and patient data for the treatment of oral cavity cancer. However, due to lack of formal verification process, it involves various inconsistencies in knowledge relevant to the formalism of knowledge, conformance to CPGs, quality of knowledge, and complexities of knowledge acquisition artifacts.Methods: This paper presents the refined knowledge acquisition (ReKA) method, which uses the Z formal verification process. The ReKA method adopts the verification method and explores the mechanism of theorem proving using the Z notation. It enhances a hybrid knowledge acquisition method to thwart the inconsistencies using formal verification.Results: ReKA adds a set of nine additional criteria to be used to have a final valid refined clinical knowledge model. These criteria ensure the validity of the final knowledge model concerning formalism of knowledge, conformance to GPGs, quality of the knowledge, usage of stringent conditions and treatment plans, and inconsistencies possibly resulting from the complexities. Evaluation, using four medical knowledge acquisition scenarios, shows that newly added knowledge in CDSS due to the additional criteria by the ReKA method always produces a valid knowledge model. The final knowledge model was also evaluated with 1229 oral cavity patient cases, which outperformed with an accuracy of 72.57% compared to a similar approach with an accuracy of 69.7%. Furthermore, the ReKA method identified a set of decision paths (about 47.8%) in the existing approach, which results in a final knowledge model with low quality, non-conformed from standard CPGs.Conclusion: ReKA refined the hybrid knowledge acquisition method by discovering the missing steps in the current validation process at the acquisition stage. As a formally proven method, it always yields a valid knowledge model having high quality, supporting local practices, and influenced by standard CPGs. Furthermore, the final knowledge model obtained from ReKA also preserves the performance such as the accuracy of the individual source knowledge models.en_US
dc.description.sponsorshipN/Aen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.urlhttps://www.sciencedirect.com/science/article/pii/S0169260720315340?via%3Dihuben_US
dc.rights© 2020 Elsevier B.V. All rights reserved.
dc.subjectKnowledge acquisitionen_US
dc.subjectClinical practice guidelinesen_US
dc.subjectData driven knowledge acquisitionen_US
dc.subjectCancer treatment planen_US
dc.subjectClinical decision support systemen_US
dc.subjectFormal verificationen_US
dc.titleAcquiring Guideline-enabled data driven clinical knowledge model using formally verified refined knowledge acquisition methoden_US
dc.typeArticleen_US
dc.contributor.departmentSejong University, Seoul, South Koreaen_US
dc.contributor.departmentOakland University, Rochester, MI, USAen_US
dc.contributor.departmentKyung Hee University, Republic of Koreaen_US
dc.contributor.departmentShaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistanen_US
dc.contributor.departmentNational Guard-Health Affairs, King Abdulaziz Medical City Riyadh, Kingdom of Saudi Arabiaen_US
dc.contributor.departmentUniversity of Derbyen_US
dc.identifier.journalComputer Methods and Programs in Biomedicineen_US
dc.identifier.eid1-s2.0-S0169260720315340
dc.identifier.piiS0169-2607(20)31534-0
dc.source.journaltitleComputer Methods and Programs in Biomedicine
dc.source.beginpage105701
dcterms.dateAccepted2020-08-05
dc.author.detail786996en_US


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