A method and system for rapid identification of adverse drug reactions based on big data

An adverse reaction and big data technology, applied in the field of emergency medicine, can solve the problems of incomplete exposure of adverse drug reactions, time-consuming, difficult access to adverse drug reaction information, etc.

Active Publication Date: 2021-03-05
THE FIRST AFFILIATED HOSPITAL ZHEJIANG UNIV COLLEGE OF MEDICINE
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Based on the characteristics of long-term clinical trial process, restrictions on combined drug use, and limited data types, it is difficult to obtain information on adverse drug reactions in scenarios such as long-term toxicity, special populations, and combined drug use, and it is not suitable for public emergencies. discovery of adverse reactions
[0004] Although the drug generation and R&D team will conduct clinical trials on the safety and effectiveness of the drug before the drug goes on the market, but limited by the strict inclusion and exclusion criteria and sample size limitations of the drug clinical trial, the clinical trial cannot fully expose the drug. Adverse reactions

Method used

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  • A method and system for rapid identification of adverse drug reactions based on big data
  • A method and system for rapid identification of adverse drug reactions based on big data
  • A method and system for rapid identification of adverse drug reactions based on big data

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Experimental program
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Embodiment 1

[0038] This embodiment provides a fast identification method for adverse drug reactions based on big data, such as figure 1 shown, including steps:

[0039] S11. Obtain adverse drug reaction data;

[0040] S12. Compare the obtained adverse drug reaction data with the pre-stored drug name ontology knowledge base and adverse reaction name ontology knowledge base respectively to generate drug-adverse reaction distributed entity vectors;

[0041] S13. According to the generated drug-adverse reaction distributed entity vector, calculate several correlation values ​​between the drug and the adverse reaction ontology;

[0042] S14. Solve the confidence interval of each correlation value according to the calculated several correlation values, and compare the confidence interval of each correlation value obtained by the solution with a preset reference value to obtain a comparison result;

[0043] S15. Judging whether the comparison result is greater than a preset threshold, if yes, ...

Embodiment 2

[0101] This embodiment provides a rapid identification system for adverse drug reactions based on big data, such as figure 2 shown, including:

[0102] An acquisition module 11, configured to acquire adverse drug reaction data;

[0103] Generating module 12, for comparing the obtained adverse drug reaction data with the pre-stored drug name ontology knowledge base and adverse reaction name ontology knowledge base respectively, to generate drug-adverse reaction distributed entity vector;

[0104] Calculation module 13, used to calculate several correlation values ​​between the drug and the adverse reaction ontology according to the generated drug-adverse reaction distributed entity vector;

[0105] The comparison module 14 is used to solve the confidence interval of each correlation degree value according to the calculated several correlation degree values, and compare the confidence interval of each correlation degree value obtained by the solution with a preset reference va...

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Abstract

The invention discloses a method and system for rapid identification of adverse drug reactions based on big data. Among them, a large data-based rapid identification method for adverse drug reactions involved in the present invention includes the steps of: S11. Acquiring adverse drug reaction data; S12. Comparing the knowledge base and the adverse reaction name ontology knowledge base to generate drug-adverse reaction distributed entity vectors; S13. According to the generated drug-adverse reaction distributed entity vectors, calculate several correlation degrees between drugs and adverse reaction ontology value; S14. According to the calculated several correlation degree values, the confidence interval of each correlation degree value is solved, and the confidence interval of each correlation degree value obtained by the solution is compared with a preset reference value to obtain a comparison result; S15 . Judging whether the comparison result is greater than a preset threshold, if yes, it is an adverse drug reaction signal; if not, it is excluded.

Description

technical field [0001] The invention relates to the technical field of emergency medicine, in particular to a method and system for quickly identifying adverse drug reactions based on big data. Background technique [0002] On the one hand, the use of antiviral drugs has played a positive role in treating human diseases and improving the health of patients; on the other hand, the adverse reactions of drugs have caused serious harm to human health in some cases. [0003] The identification of traditional drug adverse reactions involves corresponding animal experiments and various stages of phase I, II and III clinical trials, by collecting drug-related or unrelated adverse events. Based on the characteristics of long-term clinical trial process, restrictions on combined drug use, and limited data types, it is difficult to obtain information on adverse drug reactions in scenarios such as long-term toxicity, special populations, and combined drug use, and it is not suitable for...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16H10/40G16H10/20G16H70/40G06K9/62G06F16/335
CPCG16H10/20G16H10/40G16H70/40G06F16/335G06F18/29
Inventor 赵青威洪东升羊红玉张幸国倪剑胡希
Owner THE FIRST AFFILIATED HOSPITAL ZHEJIANG UNIV COLLEGE OF MEDICINE
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