Adjutant

Adjutant performs unsupervised clustering and sampling of PubMed-derived biomedical literature to support systematic literature reviews and thematic analysis.

Key Features:

• PubMed integration: Retrieves articles from PubMed using search queries to assemble literature corpora for analysis.
• Unsupervised clustering: Employs unsupervised clustering algorithms to identify and categorize topic clusters from document text.
• Sampling strategies: Implements sampling strategies after clustering to reduce dataset size for manageable downstream examination.
• Customizable speed-accuracy trade-offs: Allows adjustment of parameters to balance processing speed and analysis precision when handling large numbers of documents.
• Data preservation: Saves generated datasets produced during analysis for downstream analyses outside the tool.

Scientific Applications:

• Systematic reviews and meta-analyses: Processes large PubMed literature corpora to support study selection and thematic synthesis for systematic reviews and meta-analyses.
• Thematic discovery in biomedical research: Identifies emergent themes and topic clusters within biomedical literature to reveal research trends and knowledge gaps.
• Study prioritization and downstream analysis: Produces representative samples and preserved datasets for detailed qualitative or quantitative follow-up analyses.

Methodology:

Performs unsupervised clustering on text data extracted from PubMed articles to identify natural groupings based on thematic similarities, saves generated datasets for downstream analysis, and is underpinned by statistical techniques.