Machine learning-based methods and novel data models to predict adverse drug reaction

被引：3

作者：

Wang, Jinxian ^{[1
]}

Deng, Yuanyuan ^{[1
]}

Shu, Liang ^{[1
]}

Deng, Lei ^{[1
]}

机构：

[1] Cent South Univ, Sch Comp Sci & Engn, Changsha, Peoples R China

来源：

2020 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE | 2020年

基金：

中国国家自然科学基金;

关键词：

Adverse drug reactions (ADRs); biological features; chemical features; phenotypic features; machine learning methods; SOCIAL MEDIA; IDENTIFICATION; ASSOCIATION; INTEGRATION; VALIDATION; EXTRACTION; PROFILES;

D O I：

10.1109/BIBM49941.2020.9313093

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Predicting adverse drug reactions (ADRs) plays a critical role in developing new drugs and preventing adverse reactions during the treatment of existing drugs. However, with the rapid progress of machine learning technology, a new situation has been opened up in ADRs prediction. Using appropriate machine learning methods with existing data can achieve high prediction performance, attracting more researchers. This review describes commonly used features (biological, chemical, and phenotypic features) and machine learning algorithms.

引用

页码：1226 / 1230

页数：5

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