Department of Software, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 143-747(05006) Republic of Korea; Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA.
Department of Software, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 143-747(05006) Republic of Korea.
Comput Methods Programs Biomed. 2020 Dec;197:105701. doi: 10.1016/j.cmpb.2020.105701. Epub 2020 Aug 19.
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.
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.
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.
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.
验证和确认是临床决策支持系统（CDSS）知识获取方法的关键要求。在从各种来源获取医学知识后，需要在创建最终知识模型之前进行严格的验证和正式验证过程。此前，我们已经提出了一种混合知识获取方法，并支持从临床实践指南（CPG）和口腔癌治疗患者数据中获取医学知识的严格验证过程。但是，由于缺乏正式的验证过程，因此涉及到与知识形式化、与 CPG 一致性、知识质量和知识获取人工制品复杂性相关的各种不一致性。
本文提出了细化知识获取（ReKA）方法，该方法使用 Z 形式化验证过程。ReKA 方法采用验证方法，并使用 Z 符号探索定理证明机制。它通过使用正式验证来增强混合知识获取方法，以消除不一致性。
ReKA 添加了一组九个附加标准，用于最终获得有效的细化临床知识模型。这些标准确保了最终知识模型在知识形式化、与 GPG 一致性、知识质量、使用严格条件和治疗计划以及可能由复杂性引起的不一致性方面的有效性。使用四个医学知识获取场景进行评估表明，由于 ReKA 方法的附加标准，CDSS 中新增的知识始终会产生有效的知识模型。还使用 1229 例口腔患者病例对最终知识模型进行了评估，其准确性为 72.57%，优于具有 69.7%准确性的类似方法。此外，ReKA 方法确定了现有方法中一组决策路径（约 47.8%），导致最终知识模型质量低，不符合标准 CPG。
ReKA 通过在获取阶段发现当前验证过程中缺失的步骤来细化混合知识获取方法。作为一种经过正式证明的方法，它始终会产生具有高质量、支持本地实践并受标准 CPG 影响的有效知识模型。此外，从 ReKA 获得的最终知识模型还保留了单个源知识模型的准确性等性能。
