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Discovering Associations of Adverse Events with Pharmacotherapy in Patients with Non-Small Cell Lung Cancer Using Modified Apriori Algorithm
Joint Authors
Chen, Wei
Yang, Jun
Wang, Hui-Ling
Shi, Ya-Fei
Tang, Hao
Li, Guo-Hui
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-04-23
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Aim.
To explore the associations between adverse events and pharmacotherapy in patients with non-small cell lung cancer.
Methods.
16,527 patients with non-small cell lung cancer admitted to the Cancer Hospital, Chinese Academy of Medical Sciences, between January 1, 2010, and December 31, 2016, were included in the study.
Their medication and laboratory examinations data were extracted from the medical records.
Common Terminology Criteria for Adverse Events Version 4.03 were utilized for adverse events reporting.
A new association algorithm was developed based on Apriori algorithm and used to investigate the associations between drugs and adverse events.
In addition, a statistical comparison was conducted to compare the modified Apriori algorithm with the conventional Apriori algorithm.
Results.
Different types and levels of adverse events were identified from the abnormal laboratory findings.
The three most common adverse events were hypocalcemia, elevated creatine phosphokinase, and hypertriglyceridemia.
In addition, using the modified Apriori algorithm, 380 association rules were found between adverse events and chemotherapy.
Moreover, the statistical comparison of the two methods demonstrated that the modified Apriori algorithm was more advantageous in analyzing the correlation between drugs and adverse events than the conventional Apriori algorithm.
Conclusions.
The modified Apriori algorithm can be used to more efficiently associate pharmacotherapy with adverse events.
Based on the modified Apriori algorithm, meaningful association rules between drugs and adverse events were found, demonstrating a promising way to reveal the risk factors of adverse events during cancer treatment.
American Psychological Association (APA)
Chen, Wei& Yang, Jun& Wang, Hui-Ling& Shi, Ya-Fei& Tang, Hao& Li, Guo-Hui. 2018. Discovering Associations of Adverse Events with Pharmacotherapy in Patients with Non-Small Cell Lung Cancer Using Modified Apriori Algorithm. BioMed Research International،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1124183
Modern Language Association (MLA)
Chen, Wei…[et al.]. Discovering Associations of Adverse Events with Pharmacotherapy in Patients with Non-Small Cell Lung Cancer Using Modified Apriori Algorithm. BioMed Research International No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1124183
American Medical Association (AMA)
Chen, Wei& Yang, Jun& Wang, Hui-Ling& Shi, Ya-Fei& Tang, Hao& Li, Guo-Hui. Discovering Associations of Adverse Events with Pharmacotherapy in Patients with Non-Small Cell Lung Cancer Using Modified Apriori Algorithm. BioMed Research International. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1124183
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1124183