A Review on Adverse Drug Reaction Detection Techniques

被引:0
作者
Nafea, Ahmed A. [1 ]
AL-Mahdawi, Manar [2 ]
AL-Ani, Mohammed M. [3 ]
Omar, Nazlia [3 ]
机构
[1] Univ Anbar, Coll Comp Sci & IT, Dept Artificial Intelligence, Ramadi, Iraq
[2] Al Nahrain Univ, Coll Sci, Dept Med Phys, Baghdad 10072, Iraq
[3] Univ Kebangsaan Malaysia, Fac Informat Sci & Technol, Ctr Artificial Intelligence Technol, Bangi, Selangor, Malaysia
来源
ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY | 2024年 / 12卷 / 01期
关键词
Adverse drug reactions; Detection; Machine learning; Deep learning; Sentiment analysis; Trigger terms; SOCIAL MEDIA; BIOMEDICAL TEXT; SENTIMENT ANALYSIS; NEURAL-NETWORK; CLASSIFICATION; PHARMACOVIGILANCE; TWITTER; EVENTS; DISCOVERY; ENTITIES;
D O I
10.14500/aro.11388
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The detection of adverse drug reactions (ADRs) is an important piece of information for determining a patient's view of a single drug. This study attempts to consider and discuss this feature of drug reviews in medical opinion-mining systems. This paper discusses the literature that summarizes the background of this work. To achieve this aim, the first discusses a survey on detecting ADRs and side effects, followed by an examination of biomedical text mining that focuses on identifying the specific relationships involving ADRs. Finally, we will provide a general overview of sentiment analysis, particularly from a medical perspective. This study presents a survey on ADRs extracted from drug review sentences on social media, utilizing and comparing different techniques.
引用
收藏
页码:143 / 153
页数:11
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