asreview-systematic-review

Active learning for literature screening interactively selects and trains models on relevant papers, automating categorization to enhance efficiency and accuracy. It enables researchers to quickly sift through hundreds of research papers and focus on the most relevant ones.

Automating research paper categorization is best handled by applying active learning models to screen papers based on specific criteria. This interactive approach refines the model over time, quickly separating relevant studies from irrelevant ones during a literature review.

You need pandas, scikit-learn, and numpy to automate information retrieval for a literature review. These libraries handle data processing and model training required for the active learning screening process to function correctly.

You can use this active learning screening approach for hundreds of papers on machine learning topics. It is ideal for researchers who need to quickly sift through large volumes of papers and focus their analysis on the most relevant studies.

To start automating a systematic literature review, use the screening scripts to process papers on a specific topic and automatically categorize them into relevant and irrelevant groups based on your defined criteria.