Adverse drug reaction prediction and feature importance mining based on SIDER dataset

Tianqi Chen; Chun Liu; Mingzhe Huang; Xiang Cheng; Lixian Zhou

doi:10.1117/12.2675459

25 May 2023 Adverse drug reaction prediction and feature importance mining based on SIDER dataset

Tianqi Chen, Chun Liu, Mingzhe Huang, Xiang Cheng, Lixian Zhou

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126360D (2023) https://doi.org/10.1117/12.2675459
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

Adverse Drug Reaction (ADR) refer to harmful and irrelevant reactions that occur when normal dosage drugs are used to prevent, diagnose, treat diseases or regulate physiological functions. This definition excludes reactions caused by intentional or accidental overdose and inappropriate medication. In this paper, several models were measured and compared. The results demonstrated that base learners such as LR, SVM, RF, Adaboost, XGBoost may perform exceptionally well in some specific situations. On the other hand, if the precision of the outputs is emphasized, applying Stacking or even Multi-layer Stacking will be the most efficient tool.

Citation Download Citation

Tianqi Chen, Chun Liu, Mingzhe Huang, Xiang Cheng, and Lixian Zhou "Adverse drug reaction prediction and feature importance mining based on SIDER dataset", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126360D (25 May 2023); https://doi.org/10.1117/12.2675459

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KEYWORDS

Machine learning

Data modeling

Lawrencium

Education and training

Diseases and disorders

Statistical modeling

Performance modeling

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