LitSuggest

LitSuggest recommends relevant PubMed articles using machine learning to support biomedical literature discovery and curation.

Key Features:

• Advanced machine learning techniques: Applies text-processing and machine learning methods to identify pertinent PubMed literature beyond keyword-based search.
• Literature classification: Performs classification of articles to organize and prioritize literature for review.
• Customizable training corpus: Supports updating the training corpus to fine-tune recommendation models.
• Automated personalized weekly digests: Produces automated personalized weekly digests of recommended articles.

Scientific Applications:

• Systematic reviews: Assists in identifying and classifying articles relevant to systematic review inclusion criteria.
• Meta-analyses: Aids in assembling relevant literature sets for quantitative synthesis.
• Hypothesis generation: Facilitates discovery of relevant publications to inform hypothesis formulation.
• Literature curation: Supports ongoing curation and maintenance of up-to-date literature collections for biomedical research.

Methodology:

Applies text-processing methods and machine learning for classification and recommendation of PubMed articles, with a customizable training corpus.