Method for constructing medical business knowledge base, method and system for obtaining medical business semantic model, and medium

Abstract

The invention provides a method for constructing a medical business knowledge base, a method and a system for obtaining a medical business semantic model, and a medium. The medical business knowledgebase comprises a semantic key value pair learning network, a business type learning network and a semantic model learning network, and the method for constructing the medical business knowledge base comprises the following steps: training the semantic key value pair learning network by utilizing first training data so as to output a semantic key-value pair label set based on a phrase set; trainingthe business type learning network by utilizing second training data so as to output business type labels of the medical receipts based on the set of semantic key labels; and training the semantic model learning network by using the third training data so as to output a medical business semantic model based on the set of semantic key tags and the business type tags corresponding to the set of semantic key tags. The semantic model of the medical document can be obtained, so that further applications such as fine-grained query and medical archive management are realized.

Description

technical field [0001] The invention relates to the field of medical informatization, in particular to a method for constructing a medical service knowledge base, a method and system for obtaining a medical service semantic model, and a computer-readable medium. Background technique [0002] With the country's investment and advancement in medical informationization, residents' awareness and demand for health management of themselves and their families has gradually increased, and the demand for effective management of health-related data has also become increasingly strong. It is becoming more and more popular for residents to carry out systematic, complete and personalized health monitoring, health maintenance and health promotion for themselves or their families by comprehensively grasping health-related health information data such as outpatient visits, hospitalizations, and physical examinations. [0003] However, based on the privacy and security considerations of heal...

AI Technical Summary

Problems solved by technology

However, this kind of processing is only a coarse-grained classification of the content blocks on the medical record information, and does not introduce any semantic model and structured data corresponding to the semantic model, so it is impossible to perform fine-grained queries such as the following: user It may be expected to specifically inquire about the year, month, day, and day when the doctor prescribed aspirin, or the user may expect to specifically inquire about the aspirin prescribed by the doctor in a certain hospital. The frequency of medication to guide its medication

Not only that, the coarse-grained classification scheme of medical record information using the above-mentioned prior art does not have semantic understanding, so it is difficult to carry out follow-up more accurate data statistics, trend prediction and other applications

Images