Analyzing adverse drug reaction using statistical and machine learning methods: A systematic review.
Analyzing adverse drug reaction using statistical and machine learning methods: A systematic review.
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DOI：
10.1097/md.0000000000029387
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发表时间：
2022-06-24
期刊：
影响因子：
1.6
通讯作者：
Park, Yu Rang
中科院分区：
文献类型：
--
作者：
Kim, Hae Reong;Sung, MinDong;Park, Ji Ae;Jeong, Kyeongseob;Kim, Ho Heon;Lee, Suehyun;Park, Yu Rang
关键词：
药物不良反应 (ADR) 是药物引起的意外负面反应。确定药物与 ADR 之间的关联至关重要，并且已经提出了几种方法来证明这种关联。本系统综述旨在通过考虑利用统计和机器学习方法检测 ADR 的原始文章来检查分析工具。根据2015年至2020年发表的文章进行了系统的文献综述。使用的关键词是统计、机器学习和检测ADR信号的深度学习方法。该研究是根据系统评价和荟萃分析声明的首选报告项目 (PRISMA) 指南进行的。我们审阅了 72 篇文章，其中 51 篇和 21 篇分别涉及统计和机器学习方法。使用回归方法专门分析电子病历（EMR）数据。对于 FDA 不良事件报告系统 (FAERS) 数据，不成比例方法的组成部分更可取。 DrugBank 是最常用的机器学习数据库。其他方法占比最高，监督方法占比次之。这篇综述使用 72 篇主要文章，提供了关于哪些数据库被经常使用以及哪些分析方法可以连接的指南。统计分析时，>90%的病例采用各自发报告系统（SRS）数据或电子病历（EMR）数据进行不成比例或回归分析；然而，对于机器学习研究来说，有一种强烈的倾向来分析各种数据组合。 DrugBank数据库只占了一半，其中k近邻法所占比例最大。
Adverse drug reactions (ADRs) are unintended negative drug-induced responses. Determining the association between drugs and ADRs is crucial, and several methods have been proposed to demonstrate this association. This systematic review aimed to examine the analytical tools by considering original articles that utilized statistical and machine learning methods for detecting ADRs. A systematic literature review was conducted based on articles published between 2015 and 2020. The keywords used were statistical, machine learning, and deep learning methods for detecting ADR signals. The study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) guidelines. We reviewed 72 articles, of which 51 and 21 addressed statistical and machine learning methods, respectively. Electronic medical record (EMR) data were exclusively analyzed using the regression method. For FDA Adverse Event Reporting System (FAERS) data, components of the disproportionality method were preferable. DrugBank was the most used database for machine learning. Other methods accounted for the highest and supervised methods accounted for the second highest. Using the 72 main articles, this review provides guidelines on which databases are frequently utilized and which analysis methods can be connected. For statistical analysis, >90% of the cases were analyzed by disproportionate or regression analysis with each spontaneous reporting system (SRS) data or electronic medical record (EMR) data; for machine learning research, however, there was a strong tendency to analyze various data combinations. Only half of the DrugBank database was occupied, and the k-nearest neighbor method accounted for the greatest proportion.
影响因子：
3.8
作者：
Khong B;Lawson BO;Ma J;McGovern C;Van Atta JK;Ray A;Khong HT
通讯作者：
Khong HT
影响因子：
2
作者：
Alatawi Y;Rahman MM;Cheng N;Qian J;Peissig PL;Berg RL;Page CD;Hansen RA
通讯作者：
Hansen RA