A new iterative method to reduce workload in systematic review process

Siddhartha Jonnalagadda*, Diana Petitti

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    37 Scopus citations


    High cost for systematic review of biomedical literature has generated interest in decreasing overall workload. This can be done by applying natural language processing techniques to 'automate' the classification of publications that are potentially relevant for a given question. Existing solutions need training using a specific supervised machine-learning algorithm and feature-extraction system separately for each systematic review. We propose a system that only uses the input and feedback of human reviewers during the course of review. As the reviewers classify articles, the query is modified using a simple relevance feedback algorithm, and the semantically closest document to the query is presented. An evaluation of our approach was performed using a set of 15 published drug systematic reviews. The number of articles that needed to be reviewed was substantially reduced (ranging from 6% to 30% for a 95% recall).

    Original languageEnglish (US)
    Pages (from-to)5-17
    Number of pages13
    JournalInternational Journal of Computational Biology and Drug Design
    Issue number1-2
    StatePublished - 2013


    • Distributional semantics
    • Information retrieval
    • Machine learning
    • Systematic review

    ASJC Scopus subject areas

    • Drug Discovery
    • Computer Science Applications


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