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

Journal article


Hussain, Maqbool, Afzal, Muhammad, Ali, Taqdir, Ali, Rahman, Khan, Wajahat Ali, Jamshed, Arif, Lee, Sungyoung, Kang, Byeong Ho and Latif, Khalid 2015. Data-driven knowledge acquisition, validation, and transformation into HL7 Arden Syntax. Artificial Intelligence in Medicine. 92, pp. 51-70. https://doi.org/10.1016/j.artmed.2015.09.008
AuthorsHussain, Maqbool, Afzal, Muhammad, Ali, Taqdir, Ali, Rahman, Khan, Wajahat Ali, Jamshed, Arif, Lee, Sungyoung, Kang, Byeong Ho and Latif, Khalid
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.

KeywordsKnowledge acquisition, Knowledge validation, Prediction models, Clinical guidelines, Clinical decision support systems, HL7 Arden Syntax
Year2015
JournalArtificial Intelligence in Medicine
Journal citation92, pp. 51-70
PublisherElsevier BV
ISSN0933-3657
Digital Object Identifier (DOI)https://doi.org/10.1016/j.artmed.2015.09.008
Web address (URL)http://hdl.handle.net/10545/624843
https://www.elsevier.com/tdm/userlicense/1.0/
hdl:10545/624843
Publication dates28 Oct 2015
Publication process dates
Deposited29 May 2020, 12:17
Accepted15 Sep 2015
Rights

© 2015 Elsevier B.V. All rights reserved.

ContributorsKyung 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 and COMSATS Institute of Information Technology, Park Road, Islamabad 45550, Pakistan
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