Open AccessResearch on the adverse reactions of medicines based on deep learning models
Author(s) -
Naiwei Li,
Ziyi Cheng,
Youngmin Woo,
Jongmyung Ha,
Andre Kim
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1629/1/012102
Subject(s) - drug reaction , adverse effect , china , quality (philosophy) , risk analysis (engineering) , adverse drug reaction , drug , medicine , computer science , intensive care medicine , management science , pharmacology , engineering , political science , law , philosophy , epistemology
The frequent occurrence of adverse drug reactions in medical events has become the focus of increasing attention in various countries. The quality of adverse reaction reports is the basis for medical risk assessment and analysis. Only by obtaining high-quality and valuable information can scientific treatment be carried out. Analysis, however, at present, there is no quantitative analysis on the relationship between the factors affecting adverse drug reactions and the degree of adverse reactions. There are few reasonable mathematical models. This article adopts the method of deep learning to establish a “human-machine material law ring”. “The mathematical model between the five factors and the degree of adverse drug reactions in China analyzes adverse drug reactions in China, and provides effective guidance for reducing drug accidents and improving China’s adverse drug reaction monitoring system.