A method and device for constructing a knowledge map of specialized diseases

Abstract

This application provides a method and device for constructing a special disease knowledge map. On the basis of constructing a special disease knowledge system, the method uses a pre-built entity extraction model to extract medical entity information, and is further based on the special disease knowledge system. Extract the attribute information and relationship information of medical entities, and finally construct a special disease knowledge map based on the extracted information, wherein the entity extraction model is a model trained based on a pre-labeled training data set, and the training The data set is constructed by using medical vocabulary matching combined with manual review and labeling. In the extraction of medical entities, this application constructs the training data set required for entity extraction model training by using medical vocabulary matching combined with manual review and labeling, which can reduce the workload of constructing training data sets and reduce the impact on human resources. and time resource consumption, and at the same time, it can also improve the construction efficiency and knowledge rationality of the specialized disease knowledge map.

Description

technical field [0001] The present application belongs to the technical field of natural language processing, and in particular relates to a method and device for constructing a knowledge graph of a specific disease. Background technique [0002] Specialized disease knowledge map, which belongs to medical knowledge map, is a semantic network that describes objectively existing entities, concepts and their relationships in a certain disease field in medicine. It uses semantic technology to express systematization, structure, and integration. The knowledge in the medical field can be applied to various aspects such as disease screening and prediction, auxiliary clinical diagnosis, medical insurance risk prediction and medical knowledge popularization in the medical field. [0003] In order to have a better application in the medical field, it is very necessary to accurately construct the required specialized disease knowledge map. The construction of the medical knowledge map ...

AI Technical Summary

Problems solved by technology

At present, when constructing a knowledge map of a specific disease, in the information extraction stage, the entity extraction model is generally constructed by manually labeling the data set to realize the extraction of entity information. low defects, and this method also has high requirements for the medical background of the labeling personnel. At the same time, the quality of the labeling results is often difficult to guarantee, correspondingly, it will have a negative impact on the training of the entity extraction model, and it is difficult to guarantee the accuracy of the entity recognition. Affects the knowledge rationality of the constructed disease knowledge map

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