A big data-oriented data mining method for potential adverse drug reactions

Abstract

The invention provides a method for mining potential adverse drug reaction data from big data. The method comprises the steps of A, collecting adverse drug reaction reports; B, preprocessing data in the adverse drug reaction reports of the adverse drug reaction event data set; C, standardizing the drug name; D, filtering known adverse reaction; E, calculating degrees of association; F, sequencing the degrees of association. The method is applied to the work of mining potential adverse drug reaction and the work is not limited to the category of drugs; the potential risk of marked drugs can be effectively found out, and the method is of important significance on increasing the health level of a user.

Description

technical field

[0001] The invention relates to the field of data mining methods, and relates to a big data-oriented potential adverse drug reaction data mining method. Background technique

[0002] Adverse Drug Reactions (ADRs) have become a hot spot in the medical field and the general public, and drug safety issues have increasingly received the attention of the whole society. Although corresponding clinical trials will be carried out before the drug goes on the market, due to limitations such as the number of people and the trial cycle, clinical trials cannot reveal all the adverse reactions of the drug. As a result, new drugs with potential adverse drug reactions flow into the market, which poses a huge threat to public health. Therefore, how to mine the adverse reactions of drugs has great theoretical and practical value.

[0003] The research on mining potential adverse reactions of marketed drugs mainly relies on the electronic medical records provided by hospitals...

Images