Application of tree-based machine learning classification methods to detect signals of fluoroquinolones using the Korea Adverse Event Reporting System (KAERS) database

ABSTRACT

Background

Safety issues for fluoroquinolones have been provided by regulatory agencies. This study was conducted to identify signals of fluoroquinolones reported in the Korea Adverse Event Reporting System (KAERS) using tree-based machine learning (ML) methods.

Research design and methods

All adverse events (AEs) associated with the target drugs reported in the KAERS from 2013 to 2017 were matched with drug label information. A dataset containing label-positive and -negative AEs was arbitrarily divided into training and test sets. Decision tree, random forest (RF), bagging, and gradient boosting machine (GBM) were fitted on the training set with hyperparameters tuned using five-fold cross-validation and applied to the test set. The ML method with the highest area under the curve (AUC) scores was selected as the final ML model.

Results

Bagging was selected as the final ML model for gemifloxacin (AUC score: 1) and levofloxacin (AUC: 0.9987). RF was selected in ciprofloxacin, moxifloxacin, and ofloxacin (AUC scores: 0.9859, 0.9974, and 0.9999 respectively). We found that the final ML methods detected additional signals that were not detected using the disproportionality analysis (DPA) methods.

Conclusions

The bagging-or-RF-based ML methods performed better than DPA and detected novel AE signals previously unidentified using the DPA methods.

KEYWORDS:

• Disproportionality analysis
• fluoroquinolones
• signal detection
• spontaneous reporting system
• tree-based machine learning

Declaration of interests

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Authors contributions

MG Jang designed the study and wrote the manuscript. SH Cha, SH Kim, and SJ Lee performed statistical analyses and interpreted the data. KE Lee interpreted the data and revised the manuscript. KH Shin is the guarantor of this study and has access to data, and controlled publishing decisions. Written informed consent/assent was obtained from all authors.

Data availability statement

The following licenses/restrictions were applied to the data analyzed in this study. This study used the Korea Adverse Event Reporting System (KAERS) database established by the Korea Drug Safety Risk Management Agency (KIDS) in Korea (Number of data: 1812A0043). KIDS restricts the transfer, lease, or sale of databases to other researchers except those who have been approved for the databases provided (KIDS Official Website: https://open.drugsafe.or.kr/, Database Access Committee telephone number: [phone]).

Ethics approval

This study was approved by the Institutional Review Board (IRB) of Kyungpook National University (IRB number: KNU 2022-0214), which exempted informed consent because only deidentified data were used in this study.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/14740338.2023.2181341

Additional information

Funding

This study was conducted with the support of the National Research Foundation of Korea (NRF) grant funded by the Korean government (MIST) (NRF-2018R1D1A1B0705267) and the 4TH BK21 project (Educational Research Group of Platform Development of Management of Emerging Infectious Disease) funded by the Korean Ministry of Education (519990614732).