Medical Informatics
Research Article
Computing of umls concepts etiopathogenetic image using graph metrics
Pavel Andreevich Astanin
, Svetlana Evgen'evna Rauzina, Tat'yana Vasil'evna Zarubina
Pirogov Russian National Research Medical University|
med_cyber@mail.ru |
Abstract. At present, the development of clinical decision support (CDS) tools is a crucial task in medical informatics. A lot of different information searching algorithms are used in CDS systems. A fundamental step in the design of these algorithms is the creation of an etiopathogenetic image for the analysis of unstructured medical texts. In this paper, we have conducted the literary review and a comparative evaluation of analytical metrics used to compute the etiopathogenetic image of concepts within the graph model of the Unified Medical Language System (UMLS) metathesaurus. Subsequently, we developed and validated our version of a graph metric suitable for the aforementioned task implementation. (Linked article texts in Russian and in English).
Keywords: hospital information system, information searching algorithms, knowledge base, graph theory, UMLS
MSC-2020
68T30; 92C50
Acknowledgments: the current study has been performed within the framework of the Federal program «Priority 2030» based on the Healthcare Digital Transformation Institute (HDTI) in the Pirogov Russian National Research Medical University.
For citation: Pavel A. Astanin, Svetlana E. Rauzina, Tat'yana V. Zarubina. Computing of umls concepts etiopathogenetic image using graph metrics. Program Systems: Theory and Applications, 2023, 14:3, pp. 59–94. (In Russ., in Engl.). https://psta.psiras.ru/2023/3_59-94.
Full text of article (PDF): https://psta.psiras.ru/read/psta2023_3_59-94.pdf.
The article was submitted 30.03.2023; approved after reviewing 05.05.2023; accepted for publication 18.06.2023; published online 07.10.2023.
