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Breast cancer risk factor knowledge system model, knowledge graph system and construction method

A technology of risk factors and knowledge graphs, applied in reasoning methods, biological neural network models, neural architectures, etc., can solve the problems of too simple, difficult to apply, and limited practical application exploration, and to improve the social Overall health level, the effect of improving health benefits

Active Publication Date: 2022-02-01
WEST CHINA HOSPITAL SICHUAN UNIV +1
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  • Application Information

AI Technical Summary

Problems solved by technology

The practical application exploration for the development of cancer risk factor prediction model / knowledge system is relatively limited and shallow
However, the risk factor models generally used in the prior art, such as the logistic regression model based on a few variables, the complex system model of breast cancer etiology based on the concept of nomogram, and Paradigm II, all have incomplete knowledge and a scoring system for the strength of knowledge association. Too simple and other shortcomings, these models are difficult to aggregate and evaluate multi-dimensional knowledge entities, and it is difficult to carry out subsequent applications

Method used

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  • Breast cancer risk factor knowledge system model, knowledge graph system and construction method
  • Breast cancer risk factor knowledge system model, knowledge graph system and construction method
  • Breast cancer risk factor knowledge system model, knowledge graph system and construction method

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Embodiment 1

[0041] Embodiment 1 is a breast cancer risk factor knowledge graph system 100, such as figure 1 As shown, the knowledge graph system includes a knowledge system model 101, a knowledge graph 102 and a knowledge graph management module 103.

[0042] The knowledge system model 101 is used to establish a breast cancer etiological risk factor knowledge ontology system based on evidence-based medical evidence, literature knowledge and real-world medical data;

[0043] The knowledge graph 102 is used to construct a breast cancer etiological risk factor knowledge graph by establishing the interaction between multi-field risk factor entities and their pathogenic pathways according to the knowledge ontology of breast cancer etiological risk factors;

[0044] The knowledge map management module 103 is used for management of subsequent applications and development of strategy customization according to the breast cancer etiology knowledge map.

Embodiment 2

[0046] A complete knowledge system is the basis for effectively constructing a knowledge map. In order to establish a comprehensive and systematic knowledge system for breast cancer, this embodiment provides a scheme for establishing a breast cancer risk factor knowledge system model. The knowledge system model 101 mainly includes an ontology establishment module 1011 , a data acquisition module 1012 , a knowledge evaluation module 1013 , and a relationship classification module 1014 .

[0047] The breast cancer risk factor ontology building module (1011) mainly completes breast cancer risk factor ontology construction based on self-constructed ontology and existing ontology, which is used to solve the problem of lack of uniform standards for etiology description, annotation and classification extracted from literature, so as to establish a A knowledge ontology based on the full etiology of breast cancer. Such as image 3 As shown, the existing standard ontology and self-stru...

Embodiment 3

[0063] Such as Figure 4 As shown, the embodiment of the present invention provides a breast cancer etiology knowledge map system based on the breast cancer risk factor knowledge evaluation system, which can analyze the complexity of factors and the uncertainty of relationship expression in research literature related to risk factors of breast cancer etiology And the dynamics of continuous updating of factors for comprehensive and systematic objective quantification and display.

[0064] According to the application scenario of the knowledge graph, the breast cancer risk factor knowledge graph G is defined as a triplet:

[0065] G={V,E,F}

[0066] Among them, V represents the entity node set of multi-dimensional risk factors and resources (documents) in the knowledge map of breast cancer etiology, E represents the relationship set between entity nodes, and F represents the fact set, that is, a three-dimensional structure composed of entities and relations. A tuple represents...

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Abstract

The invention discloses a breast cancer risk factor knowledge system model, a breast cancer risk factor knowledge graph system based on a knowledge system and a construction method thereof, belongs to the field of big data processing, and can be applied to intelligent medical evaluation and risk prediction. The knowledge system model comprises an ontology establishment module, a data acquisition module, a knowledge evaluation module and a relationship classification module. According to the breast cancer risk factor knowledge system model and the knowledge graph system provided by the invention, structured and standardized processing can be performed on breast cancer and related risk factor knowledge, so that subsequent expanded application and maintenance are facilitated; the pathogenesis interaction relationship of the breast cancer can be effectively sorted, meanwhile, complex multi-dimensional risk factor entities and relationships can be networked, the method can be applied to an intelligent medical system based on knowledge graph data from multiple angles and multiple aspects in the future, and the hygienic benefits of screening items can be improved. Social overall health level is improved, and the system model has important research significance.

Description

technical field [0001] The present invention relates to the field of big data processing, in particular to a breast cancer risk factor knowledge system model, a breast cancer risk factor knowledge map system based on the knowledge system and its construction method, which can be applied to smart medical assessment and risk prediction. Background technique [0002] According to the latest global cancer data in 2020, breast cancer has replaced lung cancer as the world's largest cancer, ranking first among cancers that cause female deaths. And 90% of malignant tumors have no obvious symptoms in the early stage, and they are usually in the middle and late stages when they are discovered. Therefore, early detection, early prevention, and early treatment are important means to prevent cancer from occurring. In September 2019, the National Health Commission and relevant departments formulated the "Healthy China Action-Cancer Prevention and Control Implementation Plan (2019-2022)", ...

Claims

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Application Information

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IPC IPC(8): G06F16/36G06F16/35G06N3/04G06N5/04G16H50/70
CPCG06F16/367G06N5/04G06F16/35G16H50/70G06N3/045Y02A90/10
Inventor 彭玉兰段磊宋琳琳左劼刘晶焰何承鑫殷晋
Owner WEST CHINA HOSPITAL SICHUAN UNIV
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