Disease inference system based on medical knowledge graph

Abstract

The embodiment of the invention provides a disease inference system based on a medical knowledge graph, wherein the system includes an acquisition module for obtaining diagnosis evidence information of patients, and a disease inference module for querying the medical knowledge graph according to the diagnosis evidence information of the patients, obtaining the clinical manifestation correspondingto the diagnosis evidence information, querying the medical knowledge graph according to the clinical manifestation and obtaining disease information corresponding to the clinical manifestation; the medical knowledge graph is a mapping map established in advance according to a disease knowledge base and the professional knowledge of doctors and used for representing the interrelationship among thediagnosis evidence entity, the clinical manifestation entity and the suspected disease entity.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a disease reasoning system based on a medical knowledge map. Background technique [0002] Traditional disease reasoning systems are mostly rule-based, requiring doctors and experts to formulate a large number of rules based on medical knowledge and clinical experience, and these rules are not universal, and completing disease reasoning requires a lot of expert work. However, the latest disease reasoning system based on deep learning technology requires a large amount of medical records or data to train the model, and the reasoning results are not interpretable, so it is difficult to be accepted. Contents of the invention [0003] To solve the problems in the prior art, the embodiment of the present invention provides a disease reasoning system based on medical knowledge graph. [0004] In the first aspect, an embodiment of the present invention provides a disease inference ...

AI Technical Summary

Problems solved by technology

[0002] Traditional disease reasoning systems are mostly rule-based, requiring doctors and experts to formulate a large number of rules based on medical knowledge and clinical experience, and these rules are not universal, and completing disease reasoning requires a lot of expert work

However, the latest disease reasoning system based on deep learning technology requires a large amount of medical records or data training models, and the reasoning results are not interpretable, so it is difficult to be accepted

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