Construction method of thyroid cancer disease ontology model

Abstract

The invention discloses a construction method of a thyroid cancer disease ontology model. The construction method comprises the following steps: S100, collecting information; s200, preprocessing the information, unifying entities with different names in the information from different sources, and performing semantic extraction on the information to obtain thyroid cancer data; s300, classifying thethyroid cancer data, wherein the thyroid cancer data comprises entity class data and entity relationship class data; s400, screening and filtering the entity class data to obtain basic entity data; s500, based on the basic entity data, constructing a thyroid cancer disease ontology model according to the entity relationship class data obtained in the step S300; and S600, mapping the thyroid cancer disease ontology model to MeSH. Disease knowledge of thyroid cancer is systematically organized and analyzed, disease core concepts and attributes, semantic relations and domain knowledge axioms ofthe disease core concepts are determined, a framework system model of Chinese and English corresponding ontology concepts in the thyroid cancer disease domain is established, and preconditions are created for constructing a thyroid cancer disease knowledge base system.

Description

technical field [0001] The invention relates to the field of disease ontology models, in particular to a method for constructing a disease ontology model of thyroid cancer. Background technique [0002] Compared with other types of knowledge, medical knowledge has the characteristics of timing, conceptual diversity, high consistency requirements, short half-life, huge complexity, knowledge diversity, and ambiguity. The traditional knowledge organization method makes the research on medical knowledge often limited to a certain range of medical specialties, and separates the medical knowledge that is interrelated in a substantial sense. It becomes an obstacle for people to obtain comprehensive and accurate information, which is not convenient for medical science. Comprehensive utilization of knowledge. Therefore, how to deeply define, analyze and identify the content characteristics of medical knowledge units and their complex semantic relationships; Satisfying people's info...

AI Technical Summary

Problems solved by technology

Because the disease ontology covers a wide range of areas, is highly specialized, and requires multidisciplinary cooperation, the construction period is long and difficult to construct. Most of the existing public disease ontology in the world are upper-level ontologies, such as degenerative disease ontology, rare disease ontology, and infectious disease ontology. , oral disease ontology, etc., mostly describe the more abstract semantics in their profession, and the clinical practicability is not high

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