Clinical decision support system for hypertension medication based on knowledge graph. (December 2022)

• Record Type: Journal Article
• Authors: Zhou, Gengxian; E, Haihong; Kuang, Zemin; Tan, Ling; Xie, Xiaoxuan; Li, Jundi; Luo, Haoran
• Is Part Of: Computer methods and programs in biomedicine. Volume 227(2022)
• Publication Date: 2022-12
• Subjects: Hypertension medication -- Clinical decision support system -- Knowledge graph -- Knowledge representation; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107220
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We propose the first hypertension medication hyper-relational knowledge graph based on Chinese hypertension guidelines to provide medication decision support. Knowledge graphs endow a clinical decision support system with high visualization and explainability. Precise and complex medical knowledge can be represented by hyper-relational knowledge graphs. Medication can be implemented through predefined reasoning paths in the knowledge graph. Abstract: Background: High prevalence of hypertension and complicated medication knowledge have presented challenges to hypertension clinicians and general practitioners. Clinical decision support systems (CDSSs) are developed to aid clinicians in decision making. Current clinical knowledge is stored in fixed templates, which are not intuitive for clinicians and limit the knowledge reusability. Knowledge graphs (KGs) store knowledge in a way that is not only intuitive to humans but also processable by computers directly. However, existing medical KGs such as UMLS and CMeKG are general purpose and thus lack enough knowledge to enable hypertension medication. Methods: We first construct a KG specific to hypertension medication according to the Chinese hypertension guideline and then develop the corresponding CDSS to implement hypertension medication and knowledge management. Current advances in knowledge graph representation and modelling are researched and applied in the complex medical knowledge representation. Traditional knowledge representation and KG representation are innovatively combined in the storage of the KG to enable convenient knowledge management and easy application by the CDSS. Along a predefined reasoning path in the KG, the CDSS finally accomplishes the hypertension medication by applying knowledge stored in the KG. 124 health records of a hypertension Chief Physician from Beijing Anzhen Hospital, Capital Medical University, are collected to evaluate the system metrics on the single drug recommendation task. Results and conclusion: The proposed CDSS has functions of medication knowledge graph management and hypertension medication decision support. With elaborate design on knowledge representation, knowledge management is intuitive and convenient. By virtue of the KG, medication recommendations are highly visualized and explainable. Experiments on 124 health records with 90% guideline compliance collected from hospitals in single class recommendation task achieve 91%, 83% and 77% on recall, hit@3 and MRR metrics respectively, which demonstrates the quality of the KG and effectiveness of the system.