Method, device and system for information classification based on Bayesian structure learning

Abstract

The invention discloses a method, a device and a system for information classification based on Bayesian structure learning. The method comprises the steps: extracting information according to a material provided by a user, arranging the information into a structured data list, obtaining an incidence relation of all diseases through Bayesian structure learning according to a historical user data set and an information database, constructing a Bayesian network structure, aggregating the front and back sequence diseases belonging to the same type in the Bayesian network to obtain a corresponding disease classification, and forming a corresponding strategy scheme according to an expert experience knowledge graph to output an analysis result to the user. The method exacts information from materials such as user medical records based on Bayesian structure learning, performs structured processing on the extracted data, obtains association relationships of each disease in combination with a historical data set and an information database and forms a corresponding reference strategy scheme in combination with the expert experience knowledge graph, and the scheme can be important reference information to assist doctors in judgment, so as to save processing time.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to an information classification method, device and system based on Bayesian structure learning. Background technique [0002] The classification and analysis of diseases is a necessary step in many scenarios. The current classification of diseases mainly relies on manual judgment, and the professional ability of professionals is uneven, which leads to confusion in the classification. The classification may be because the medical profession is not deep enough. For example, there are many causes of cancer. Doctors may have different expressions for thyroid cancer when issuing diagnosis certificates, such as goiter mass, thyroid malignant tumor, thyroid papillary malignant tumor, etc. These expressions are different. Corresponding to certain specific periods or types of thyroid cancer, but if the professionalism is not enough, it is easy to cause misclassification. Thi...

AI Technical Summary

Problems solved by technology

[0002] The classification and analysis of diseases is a necessary step in many scenarios. The current classification of diseases mainly relies on manual judgment, and the professional ability of professionals is uneven, which leads to confusion in the classification. The classification may be because the medical profession is not deep enough. For example, there are many causes of cancer. Doctors may have different expressions for thyroid cancer when issuing diagnosis certificates, such as goiter mass, thyroid malignant tumor, thyroid papillary malignant tumor, etc. These expressions are different. Corresponding to certain specific periods or types of thyroid cancer, but if the professionalism is not enough, it is easy to cause misclassification. This classification difficulty is more obvious in mental diseases. Therefore, it is necessary to use data-driven methods to assist in reasonable disease classification. Improve secondary reference reliability

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