Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Guideline-Enabled Data Driven Clinical Knowledge Model for the Treatment of Oral Cavity Cancer Acquired Through a Refined Knowledge Acquisition Method

Version 1 : Received: 15 June 2019 / Approved: 19 June 2019 / Online: 19 June 2019 (07:57:05 CEST)
Version 2 : Received: 19 July 2019 / Approved: 22 July 2019 / Online: 22 July 2019 (06:17:34 CEST)

A peer-reviewed article of this Preprint also exists.


Validation and verification are the critical requirements in the knowledge acquisition method for 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 for acquiring medical knowledge from clinical practice guidelines (CPGs) and patient data in the Smart CDSS for treatment of oral cavity cancer. The final knowledge model was created by combining knowledge models obtained from CPGs and patient data after passing through a rigorous validation process. However, detailed analysis shows that 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. Therefore, it is required to enhance a hybrid knowledge acquisition method that thwarts the inconsistencies using formal verification. This paper presents the verification process using the Z formal method and its outcome as an enhanced acquisition method – known as the refined knowledge acquisition (ReKA) method. The ReKA method adopted verification method and explored the mechanism of theorem proving using the Z notation. It enables to identify inconsistencies in the validation process used for hybrid knowledge acquisition. Additionally, it refines the hybrid knowledge acquisition method by discovering the missing steps in the current validation process at the acquisition stage. Consequently, ReKA adds a set of nine additional criteria to be used to have a final valid refined clinical knowledge model. The criteria ensure the validity of 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 addition of criteria by 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, 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. In conclusion, ReKA is formally proved method which always yields valid knowledge model having high quality, supporting local practices, and influenced from standard guidelines.


knowledge acquisition; clinical practice guidelines; data driven knowledge acquisition; cancer treatment plan; clinical decision support system; formal verification


Computer Science and Mathematics, Probability and Statistics

Comments (1)

Comment 1
Received: 9 September 2020
The commenter has declared there is no conflict of interests.
Comment: Dear Colleagues,

This paper is now published at Computer Methods and Programs in Biomedicine (an Elsevier Journal). The paper has gone through rigorous review process and the published version has been significantly improved. The title of the published version is also changed during review process. Kindly visit the following link to access the final version of this paper;

Revised Title of paper:
Acquiring guideline-enabled data driven clinical knowledge model using formally verified refined knowledge acquisition method

Link to paper:
Thank you for your interest in our work!!!
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