Artificial Neural Network and Latent Semantic Analysis for Adverse Drug Reaction Detection

被引:2
|
作者
Nafea, Ahmed Adil [1 ]
Omar, Nazlia [1 ]
Al-qfail, Zohaa Mubarak [1 ]
机构
[1] Univ Kebangsaan Malaysia, Fac Informat Sci & Technol, Ctr Artificial Intelligence Technol CAIT, Bangi, Selangor, Malaysia
关键词
Adverse Drug Reaction; Artificial Neural Network; Classification; Deep Learning; Latent Semantic Analysis; PLAYFAIR;
D O I
10.21123/bsj.2023.7988
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
Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting ADR.
引用
收藏
页码:226 / 233
页数:8
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