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    Data-driven knowledge acquisition, validation, and transformation into HL7 Arden Syntax

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    Authors
    Hussain, Maqbool
    Afzal, Muhammad
    Ali, Taqdir
    Ali, Rahman
    Khan, Wajahat Ali
    Jamshed, Arif
    Lee, Sungyoung
    Kang, Byeong Ho
    Latif, Khalid
    Affiliation
    Kyung Hee University, Seocheon-dong, Giheung-gu, Yongin-si 446-701, Gyeonggi-do, Republic of Korea
    Shaukat Khanum Memorial Cancer Hospital and Research Centre, 7A Block R-3, M.A. Johar Town, Lahore 54782, Pakistan
    University of Tasmania, Hobart 7001, Tasmania, Australia
    COMSATS Institute of Information Technology, Park Road, Islamabad 45550, Pakistan
    Issue Date
    2015-10-28
    
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    Abstract
    The 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.
    Citation
    Hussain, 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.
    Publisher
    Elsevier BV
    Journal
    Artificial Intelligence in Medicine
    URI
    http://hdl.handle.net/10545/624843
    DOI
    10.1016/j.artmed.2015.09.008
    Additional Links
    https://www.sciencedirect.com/science/article/pii/S0933365715001256?casa_token=t1c0Oasl7p4AAAAA:TQfFcvZPCPomW_GQyp7LM6JwCyecirDPGmS-1vh0Z9rvsZgYR0zS-W2usmV_gfNRMqqPf_cq5A
    http://uclab.khu.ac.kr/resources/publication/J_214.pdf
    Type
    Article
    Language
    en
    ISSN
    0933-3657
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.artmed.2015.09.008
    Scopus Count
    Collections
    Department of Electronics, Computing & Maths

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