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Method and system for predicting disease risk

A disease risk and risk prediction technology, applied in the field of artificial intelligence, can solve problems such as limiting the accuracy of disease prediction, and achieve the effects of reducing noise interference, improving accuracy, and improving model robustness

Pending Publication Date: 2022-04-19
PING AN TECH (SHENZHEN) CO LTD
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AI Technical Summary

Problems solved by technology

The existing PRS (Polygene risk score, polygenic risk score) assumes the generalized linearity of each SNP locus on the risk of schizophrenia, which limits the prediction accuracy of the disease

Method used

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  • Method and system for predicting disease risk
  • Method and system for predicting disease risk
  • Method and system for predicting disease risk

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Embodiment Construction

[0053] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0054] Acquisition and processing of relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0055] Artificial intelligence basic te...

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Abstract

The invention provides a disease risk prediction method and system, and the method comprises the steps: obtaining gene variation site single nucleotide polymorphism (SNP) characterization information and clinical phenotype feature information of a disease patient, and constructing a data set based on the SNP characterization information and the clinical phenotype feature information; building a risk prediction basic model based on a neural network; training the risk prediction basic model by using the data set to obtain an intelligent risk prediction model for predicting the disease risk probability; and performing performance evaluation on the intelligent risk prediction model. According to the scheme, deep learning is utilized to learn SNP characterization of a disease patient, meanwhile, the incidence relation between the SNP site and the disease can be captured through the deep learning model, and the accuracy of disease risk prediction can be effectively improved.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a method and system for predicting disease risk, a storage medium, and a computing device. Background technique [0002] Schizophrenia is the most common mental disease, and it is also the most complicated mental disease with the most complex etiology and clinical manifestations. It is estimated that the heritability of schizophrenia is about 80%, and the incidence rate is about 1% worldwide. . Although schizophrenia has been studied for a long time, its pathogenic mechanism has not been clarified, so clinical treatment mainly focuses on symptoms rather than etiology, which makes researchers focus on the research on the mechanism of schizophrenia for many years . [0003] The recent rise of genomics, transcriptomics, proteomics, and metabolomics has injected new vitality into the study of the pathogenesis of schizophrenia. The research has achieved a se...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/30G16B20/20G06N3/04G06N3/08
CPCG16H50/30G16B20/20G06N3/084G06N3/045
Inventor 李映雪
Owner PING AN TECH (SHENZHEN) CO LTD
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