Application of Association Rules Analysis in Mining Adverse Drug Reaction Signals

Adverse drug reactions (ADRs) are increasingly becoming a serious public health problem. Spontaneous reporting systems (SRSs) are an important way for many countries to monitor ADRs produced in the clinical use of drugs, and they are the main data source for ADR signal detection. The traditional sig...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jianxiang Wei, Jimin Dai, Yingya Zhao, Pu Han, Yunxia Zhu, Weidong Huang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	association rule data mining adverse drug reaction signal detection Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/3ddb84ef22b34377a68036ea66bf3eef
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:3ddb84ef22b34377a68036ea66bf3eef
record_format	dspace
spelling	oai:doaj.org-article:3ddb84ef22b34377a68036ea66bf3eef2021-11-25T16:38:47ZApplication of Association Rules Analysis in Mining Adverse Drug Reaction Signals10.3390/app1122108282076-3417https://doaj.org/article/3ddb84ef22b34377a68036ea66bf3eef2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10828https://doaj.org/toc/2076-3417Adverse drug reactions (ADRs) are increasingly becoming a serious public health problem. Spontaneous reporting systems (SRSs) are an important way for many countries to monitor ADRs produced in the clinical use of drugs, and they are the main data source for ADR signal detection. The traditional signal detection methods are based on disproportionality analysis (DPA) and lack the application of data mining technology. In this paper, we selected the spontaneous reports from 2011 to 2018 in Jiangsu Province of China as the research data and used association rules analysis (ARA) to mine signals. We defined some important metrics of the ARA according to the two-dimensional contingency table of ADRs, such as Confidence and Lift, and constructed performance evaluation indicators such as Precision, Recall, and F1 as objective standards. We used experimental methods based on data to objectively determine the optimal thresholds of the corresponding metrics, which, in the best case, are Confidence = 0.007 and Lift = 1. We obtained the average performance of the method through 10-fold cross-validation. The experimental results showed that F1 increased from 31.43% in the MHRA method to 40.38% in the ARA method; this was a significant improvement. To reduce drug risk and provide decision making for drug safety, more data mining methods need to be introduced and applied to ADR signal detection.Jianxiang WeiJimin DaiYingya ZhaoPu HanYunxia ZhuWeidong HuangMDPI AGarticleassociation ruledata miningadverse drug reactionsignal detectionTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10828, p 10828 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	association rule data mining adverse drug reaction signal detection Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	association rule data mining adverse drug reaction signal detection Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Jianxiang Wei Jimin Dai Yingya Zhao Pu Han Yunxia Zhu Weidong Huang Application of Association Rules Analysis in Mining Adverse Drug Reaction Signals
description	Adverse drug reactions (ADRs) are increasingly becoming a serious public health problem. Spontaneous reporting systems (SRSs) are an important way for many countries to monitor ADRs produced in the clinical use of drugs, and they are the main data source for ADR signal detection. The traditional signal detection methods are based on disproportionality analysis (DPA) and lack the application of data mining technology. In this paper, we selected the spontaneous reports from 2011 to 2018 in Jiangsu Province of China as the research data and used association rules analysis (ARA) to mine signals. We defined some important metrics of the ARA according to the two-dimensional contingency table of ADRs, such as Confidence and Lift, and constructed performance evaluation indicators such as Precision, Recall, and F1 as objective standards. We used experimental methods based on data to objectively determine the optimal thresholds of the corresponding metrics, which, in the best case, are Confidence = 0.007 and Lift = 1. We obtained the average performance of the method through 10-fold cross-validation. The experimental results showed that F1 increased from 31.43% in the MHRA method to 40.38% in the ARA method; this was a significant improvement. To reduce drug risk and provide decision making for drug safety, more data mining methods need to be introduced and applied to ADR signal detection.
format	article
author	Jianxiang Wei Jimin Dai Yingya Zhao Pu Han Yunxia Zhu Weidong Huang
author_facet	Jianxiang Wei Jimin Dai Yingya Zhao Pu Han Yunxia Zhu Weidong Huang
author_sort	Jianxiang Wei
title	Application of Association Rules Analysis in Mining Adverse Drug Reaction Signals
title_short	Application of Association Rules Analysis in Mining Adverse Drug Reaction Signals
title_full	Application of Association Rules Analysis in Mining Adverse Drug Reaction Signals
title_fullStr	Application of Association Rules Analysis in Mining Adverse Drug Reaction Signals
title_full_unstemmed	Application of Association Rules Analysis in Mining Adverse Drug Reaction Signals
title_sort	application of association rules analysis in mining adverse drug reaction signals
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3ddb84ef22b34377a68036ea66bf3eef
work_keys_str_mv	AT jianxiangwei applicationofassociationrulesanalysisinminingadversedrugreactionsignals AT jimindai applicationofassociationrulesanalysisinminingadversedrugreactionsignals AT yingyazhao applicationofassociationrulesanalysisinminingadversedrugreactionsignals AT puhan applicationofassociationrulesanalysisinminingadversedrugreactionsignals AT yunxiazhu applicationofassociationrulesanalysisinminingadversedrugreactionsignals AT weidonghuang applicationofassociationrulesanalysisinminingadversedrugreactionsignals
_version_	1718413071385362432

Application of Association Rules Analysis in Mining Adverse Drug Reaction Signals

Ejemplares similares