A Method for Entity Disambiguation in Medical Disease Diagnosis Records

Abstract

The invention discloses a method for disambiguating entity names in a medical disease diagnosis record. Based on a heterogeneous concomitant disease network and a graph model, the entity names in the medical disease diagnosis record are disambiguated. The similarity between to-be-disambiguated entity names and candidate entity names is used as local information, and the contribution of other to-be-disambiguated entities in the same record to current to-be-disambiguated entities serves as global information, so that the accuracy of medical entity name disambiguation can be improved; the heterogeneous concomitant disease network is established according to the disease diagnosis record and annotation data, so that the relationships between the diseases and between the disease and the operation can be reflected more intuitively and credibly; and the entity names are subjected to standard name mapping accurately and efficiently, so that the problem of ambiguity of medical disease entity names in diagnosis information is solved, and the practical application demands are met.

Description

technical field [0001] The invention relates to the fields of natural language text information processing and medical big data mining, in particular to a method for disambiguating disease entities and operation entities based on medical disease diagnosis records. Background technique [0002] Medical disease diagnosis records include the name of the main disease diagnosed by the patient, the name of the secondary diagnosed disease (ie, the name of the accompanying disease), and the operation for the diagnosis of the disease. For the same disease name, due to the variety of diseases and differences in doctor experience, there are often many different expressions for the same disease name, which brings great challenges to the standardization of medical electronic medical record data. [0003] The task of named entity disambiguation is to establish a mapping relationship with the corresponding entity in the knowledge base for a given entity reference in the text (reference ref...

AI Technical Summary

Problems solved by technology

The supervised learning method can use the labeling information and the characteristics of the training data to mine the regularity between the entity and the candidate entity. The disadvantage is that it needs to manually label the data, and the cost is high; Need to label data, can use the semantic information of the entity context, but feature extraction is more difficult

[0004] At present, the research on named entity disambiguation is becoming more and more mature, but for the disambiguation task of disease entities in the medical field, due to the diversification of disease names and incomplete diagnostic information, it is impossible to directly use the method of general named entity disambiguation. Therefore, there is no Effective Disambiguation Model and Method for Disease Entity in Medical Field

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