ADR auxiliary decision-making system based on deep learning

Abstract

The invention discloses an ADR auxiliary decision-making system based on deep learning, and the system comprises a drug database module which is used for obtaining and storing drug information and related adverse drug reaction information; the CHPS drug evaluation module is used for exporting perfect adverse drug reaction data on the basis of a CHPS system; the deep learning module is used for learning adverse drug reaction data exported from the CHPS drug evaluation module; the auxiliary evaluation and risk early warning module is used for comparing the input certain medicine information by using the deep learning module; the medication decision-making module is used for carrying out medication decision-making judgment after passing through the auxiliary evaluation and risk early warning module and giving information about whether medication is carried out or not; the feedback module is used for feeding back the related physical sign data of the patient after medication and the dosage and frequency data of medication to the deep learning module; the system can make decision analysis and risk management on medication based on deep learning while improving and perfecting adverse drug reaction data.

Description

technical field [0001] The invention belongs to the technical field of drug monitoring, and in particular relates to an ADR auxiliary decision-making system based on deep learning. Background technique [0002] Adverse Drug Reaction (ADR) refers to a harmful but not expected reaction that has a causal relationship with the drug application during the normal application of the drug at the prescribed dose. Adverse drug reactions can generally be divided into four categories: side effects, toxic reactions, allergic reactions, and secondary infections. Adverse reactions will endanger human monitoring, and even endanger human life in serious cases. Therefore, the detection of adverse drug reaction rate is of great significance to ensure drug safety and public health. [0003] In order to monitor and collect adverse drug reactions, the Chinese Hospital Pharmacovigilance System (CHPS) was introduced accordingly. The main function of the CHPS drug evaluation system is to quickly o...

AI Technical Summary

Problems solved by technology

In the ADR report, the ADR report data is insufficient and the quality is not high

In addition, there are difficulties in reviewing and tracing the reported data; at the same time, the existing CHPS system is currently unable to perform decision-making analysis and management. We need to solve problems related to ADR from a technical level

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