Drug specification-based reasonable medication knowledge graph construction method

Abstract

The invention discloses a drug specification-based reasonable medication knowledge graph construction method, which comprises the following steps of S10, extracting a drug specification, and inducingentities and relationships in the drug specification through an expert labeling method to form an entity and relationship indexing rule base; S20, training a machine learning model through a semi-supervised learning method on the basis of data labeled by experts and machine learning rules; and S30, using the trained machine learning model to predict and label the unlabeled drug specification to form a knowledge graph of the drug relationship. According to the method, natural language processing and knowledge graph technologies are utilized, and the reasonable medication knowledge base is constructed based on the medicine specification, so that medication errors can be avoided, medical risks can be prevented, and the clinical medication safety can be improved. The method has an automatic construction capability, and the construction precision is improved through machine learning.

Description

technical field [0001] The invention relates to the field of knowledge graphs, in particular to a method for constructing rational drug use knowledge graphs based on drug instructions. Background technique [0002] With the advancement of science and the development of the times, the explosion of knowledge has posed severe challenges to the work of physicians. The update and growth of knowledge in the medical field has also exceeded the limits of physicians' learning and mastery. The diversity of drugs and the different pathological characteristics of patients complicate drug treatment. Many factors will affect the type and dosage of drugs. It is often not enough to rely solely on the personal judgment of doctors. The traditional Chinese pharmacy knowledge base of the rational drug use system mainly relies on manual construction by professionals, the labor cost is high, and the accuracy of the knowledge base is not high, which cannot meet the requirements of the hospital's e...

AI Technical Summary

Problems solved by technology

Although there are methods for constructing pharmaceutical knowledge graphs in the prior art, these knowledge graphs are constructed by extracting structured data manually or by regular expression rules. Although the manual method extracts more accurate structured knowledge, it consumes manpower and time. Many, and manual long-term operation is easy to cause fatigue and error

Although the method based on regular expression rules relies on the machine to save time, the regular method is only suitable for simple rules. For the complex situation of Chinese, the error of regular expressions will be relatively high

[0004] Drug instructions are one of the important sources of drug information, and are also the scientific basis for physicians, pharmacists, nurses, and patients to treat medication. However, all drug entity relationships (including routes of administration, repeated medication, and drug interactions , indications, adverse reactions, contraindications, medication for the elderly, medication for children, medication for pregnancy, etc.) There is still little research on constructing a knowledge map for reasoning from drug-diagnosis-clinical diagnosis and treatment data

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