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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/22/10828 |
| _version_ | 1797511277025689600 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:44:01Z |
| format | Article |
| id | doaj.art-3ddb84ef22b34377a68036ea66bf3eef |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T05:44:01Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3ddb84ef22b34377a68036ea66bf3eef2023-11-22T22:19:27ZengMDPI AGApplied Sciences2076-34172021-11-0111221082810.3390/app112210828Application of Association Rules Analysis in Mining Adverse Drug Reaction SignalsJianxiang Wei0Jimin Dai1Yingya Zhao2Pu Han3Yunxia Zhu4Weidong Huang5School of Management, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaSchool of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaSchool of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaSchool of Management, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaSchool of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaSchool of Management, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaAdverse 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.https://www.mdpi.com/2076-3417/11/22/10828association ruledata miningadverse drug reactionsignal detection |
| spellingShingle | Jianxiang Wei Jimin Dai Yingya Zhao Pu Han Yunxia Zhu Weidong Huang Application of Association Rules Analysis in Mining Adverse Drug Reaction Signals Applied Sciences association rule data mining adverse drug reaction signal detection |
| title | 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_short | Application of Association Rules Analysis in Mining Adverse Drug Reaction Signals |
| title_sort | application of association rules analysis in mining adverse drug reaction signals |
| topic | association rule data mining adverse drug reaction signal detection |
| url | https://www.mdpi.com/2076-3417/11/22/10828 |
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