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A Review of Drug Side Effect Identification Methods
Shuai Deng1, Yige Sun2, Tianyi Zhao3
1College of Science, Beijing Forestry University, Beijing, China.
Current Pharmaceutical Design
|June 14, 2020
Summary
Identifying drug side effects is crucial for drug safety. This review categorizes methods like biological experiments, machine learning, text mining, and network approaches to improve adverse drug reaction detection.
Area of Science:
- Pharmacovigilance and Drug Safety
- Computational Biology
- Bioinformatics
Background:
- Drug side effects are key indicators of drug safety.
- Insufficient clinical understanding and lengthy clinical trials hinder timely reporting of adverse drug reactions.
- Developing effective methods for identifying drug side effects is a critical research area.
Purpose of the Study:
- To review and categorize existing methods for identifying drug side effects.
- To discuss common databases used in drug side effect identification.
- To highlight the advantages and disadvantages of various identification approaches.
Main Methods:
- Biological experimental methods
- Machine learning approaches
- Text mining techniques
- Network-based methods
Main Results:
- Methods for identifying drug side effects were classified into four main categories.
- Key aspects, advantages, and disadvantages of each method category were detailed.
- Commonly used databases in this field were identified.
Conclusions:
- A comprehensive overview of drug side effect identification methods is provided.
- The review offers insights into the strengths and weaknesses of different approaches.
- Future research directions in drug safety and side effect detection are proposed.
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