Comparison of Machine Learning Methods Applied to Estimation of Side Effect in Drug Interaction Using Japanese Adverse Drug Event Report (JADER) Database
- VernacularTitle:医薬品副作用データベースを用いた医薬品相互作用での有害事象推定へ適用する機械学習手法の比較
- Author:
Ryo TSUTSUI
1
;
Ryo ONODA
2
;
Sumio MATZNO
2
;
Naoki OHBOSHI
3
Author Information
- Keywords: JADER; support Vector Machine; deep Neural Network; random forest; drug interaction
- From:Japanese Journal of Drug Informatics 2020;22(3):123-130
- CountryJapan
- Language:Japanese
- Abstract: Objective: In this study, we analyzed the Japanese Adverse Drug Event Report (JADER) database in order detect unexpected adverse events using three polypharmacy machine learning models.Methods: The patient’s age, weight, height, gender, date and time of onset, subsequent appearance, and the taking medicines were preprocessed. They were applied for the prediction of adverse events using three machine learning procedures such as support vector machine (SVM), deep neural network (DNN) and random forest (RF).Results: Precision, matching, reproduction and F-values were almost same between the three techniques. Polypharmacy effects were predicted in approximately 80% of adverse events. Unexpected predictions were observed between DNN and RF, but different from SVM.Conclusion: Results suggest that the combination of DNN or RF and SVM can yield accurate predictions. We also suggest that RF is more useful because of its easy validation.
