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dc.contributor.authorHussain, Maqbool
dc.contributor.authorAfzal, Muhammad
dc.contributor.authorAli, Taqdir
dc.contributor.authorAli, Rahman
dc.contributor.authorKhan, Wajahat Ali
dc.contributor.authorJamshed, Arif
dc.contributor.authorLee, Sungyoung
dc.contributor.authorKang, Byeong Ho
dc.contributor.authorLatif, Khalid
dc.date.accessioned2020-05-29T12:17:15Z
dc.date.available2020-05-29T12:17:15Z
dc.date.issued2015-10-28
dc.identifier.citationHussain, M., Afzal, M., Ali, T., Ali, R., Khan, W.A., Jamshed, A., Lee, S., Kang, B.H. and Latif, K., (2018). 'Data-driven knowledge acquisition, validation, and transformation into HL7 Arden Syntax'. Artificial intelligence in medicine, 92, pp. 51-70.en_US
dc.identifier.issn0933-3657
dc.identifier.doi10.1016/j.artmed.2015.09.008
dc.identifier.urihttp://hdl.handle.net/10545/624843
dc.description.abstractThe objective of this study is to help a team of physicians and knowledge engineers acquire clinical knowledge from existing practices datasets for treatment of head and neck cancer, to validate the knowledge against published guidelines, to create refined rules, and to incorporate these rules into clinical workflow for clinical decision support. A team of physicians (clinical domain experts) and knowledge engineers adapt an approach for modeling existing treatment practices into final executable clinical models. For initial work, the oral cavity is selected as the candidate target area for the creation of rules covering a treatment plan for cancer. The final executable model is presented in HL7 Arden Syntax, which helps the clinical knowledge be shared among organizations. We use a data-driven knowledge acquisition approach based on analysis of real patient datasets to generate a predictive model (PM). The PM is converted into a refined-clinical knowledge model (R-CKM), which follows a rigorous validation process. The validation process uses a clinical knowledge model (CKM), which provides the basis for defining underlying validation criteria. The R-CKM is converted into a set of medical logic modules (MLMs) and is evaluated using real patient data from a hospital information system. We selected the oral cavity as the intended site for derivation of all related clinical rules for possible associated treatment plans. A team of physicians analyzed the National Comprehensive Cancer Network (NCCN) guidelines for the oral cavity and created a common CKM. Among the decision tree algorithms, chi-squared automatic interaction detection (CHAID) was applied to a refined dataset of 1229 patients to generate the PM. The PM was tested on a disjoint dataset of 739 patients, which gives 59.0% accuracy. Using a rigorous validation process, the R-CKM was created from the PM as the final model, after conforming to the CKM. The R-CKM was converted into four candidate MLMs, and was used to evaluate real data from 739 patients, yielding efficient performance with 53.0% accuracy. Data-driven knowledge acquisition and validation against published guidelines were used to help a team of physicians and knowledge engineers create executable clinical knowledge. The advantages of the R-CKM are twofold: it reflects real practices and conforms to standard guidelines, while providing optimal accuracy comparable to that of a PM. The proposed approach yields better insight into the steps of knowledge acquisition and enhances collaboration efforts of the team of physicians and knowledge engineers.en_US
dc.description.sponsorshipMinistry of Trade, Industry and Energyen_US
dc.language.isoenen_US
dc.publisherElsevier BVen_US
dc.relation.urlhttps://www.sciencedirect.com/science/article/pii/S0933365715001256?casa_token=t1c0Oasl7p4AAAAA:TQfFcvZPCPomW_GQyp7LM6JwCyecirDPGmS-1vh0Z9rvsZgYR0zS-W2usmV_gfNRMqqPf_cq5Aen_US
dc.relation.urlhttp://uclab.khu.ac.kr/resources/publication/J_214.pdfen_US
dc.rights© 2015 Elsevier B.V. All rights reserved.
dc.rights.urihttps://www.elsevier.com/tdm/userlicense/1.0/
dc.subjectKnowledge acquisition, Knowledge validation, Prediction models, Clinical guidelines, Clinical decision support systems, HL7 Arden Syntaxen_US
dc.titleData-driven knowledge acquisition, validation, and transformation into HL7 Arden Syntaxen_US
dc.typeArticleen_US
dc.contributor.departmentKyung Hee University, Seocheon-dong, Giheung-gu, Yongin-si 446-701, Gyeonggi-do, Republic of Koreaen_US
dc.contributor.departmentShaukat Khanum Memorial Cancer Hospital and Research Centre, 7A Block R-3, M.A. Johar Town, Lahore 54782, Pakistanen_US
dc.contributor.departmentUniversity of Tasmania, Hobart 7001, Tasmania, Australiaen_US
dc.contributor.departmentCOMSATS Institute of Information Technology, Park Road, Islamabad 45550, Pakistanen_US
dc.identifier.journalArtificial Intelligence in Medicineen_US
dc.identifier.piiS0933365715001256
dc.source.journaltitleArtificial Intelligence in Medicine
dc.source.volume92
dc.source.beginpage51
dc.source.endpage70
dcterms.dateAccepted2015-09-15
dc.author.detail786996en_US


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