ISSN Print 2500–1094
ISSN Online 2542–1204
BIOMEDICAL JOURNAL OF PIROGOV UNIVERSITY (MOSCOW, RUSSIA)
METHOD
Development of medical nomenclature and algorithms for diagnosis and treatment of gout in outpatient settings
About authors
Pirogov Russian National Research Medical University, Moscow, Russia
Correspondence should be addressed: Ivan S. Osmolovsky
Ostrovityanova, 1, Moscow, 117997; оur.kb@navi_yksvoloms
About paper
Author contribution: Osmolovsky IS built and performed technical verification of the medical nomenclature and algorithms for the diagnosis and treatment of gout, analyzed information provided by the expert panel, wrote the technical section of the paper and prepared figures and tables; Zarubina TV proposed the design of the study, supervised the study, analyzed information provided by the expert panel and performed technical verification of the informational objects; Shostak NA, Kondrashov AA, Klimenko AA collected data for the informational objects, performed clinical verification of the informational objects and wrote the clinical section of this paper.
Compliance with ethical standards: the study was approved by the Ethics Committee of Pirogov Russian National Research Medical University (Protocol № 192 dated January 27, 2020).
Received: 2021-03-03
Accepted: 2021-04-01
Published online: 2021-04-17
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Gout is a chronic systemic disease characterized by the deposition of monosodium urate crystals in various tissues and inflammation. In Russia, time to diagnosis may be as long as 8 years. This leads to serious complications, such as urate nephropathy, and disability. Effective strategies are needed to improve the quality of medical care for gout patients. One of such strategies is creation of an expert system to aid the clinician in establishing the diagnosis and selecting adequate therapy. The cornerstone of an expert system is a knowledge base. The aim of this paper was to develop a medical nomenclature and algorithms for the diagnosis and treatment of gout that will be used to create an expert system in the future. A total of 1,174 entities were selected that laid the basis for 40 diagnostic and 50 treatment algorithms for gout patients. All informational models were verified by the expert panel.
Keywords: ontology, knowledge base, expert system, CDSS, clinical decision support system, gout