ArticleInternational journal of computational biology and drug design2013
A new iterative method to reduce workload in systematic review process.
Article in International journal of computational biology and drug design, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 6 of them syntheses that pooled it.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- How much can we save by applying artificial intelligence in evidence synthesis? Results from a pragmatic review to quantify workload efficiencies and cost savings.Frontiers in pharmacology · 2025Pooled it
- Using meta-analysis and CNN-NLP to review and classify the medical literature for normal tissue complication probability in head and neck cancer.Radiation oncology (London, England) · 2024Pooled it
- Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes.JCO clinical cancer informatics · 2019Pooled it
- Crowdsourcing the Citation Screening Process for Systematic Reviews: Validation Study.Journal of medical Internet research · 2019Pooled it
- Automating data extraction in systematic reviews: a systematic review.Systematic reviews · 2015Pooled it
- Using text mining for study identification in systematic reviews: a systematic review of current approaches.Systematic reviews · 2015Pooled it
- From API to Action: A Multi-Model Comparison of OpenAI, Anthropic, Google, and Meta LLMs for Clinical Trial Data Extraction.Bioengineering (Basel, Switzerland) · 2026Article
- Study protocol for evaluating automation of systematic review processes with EPPI-Reviewer and Copilot 365 in updating the cataract evidence gap map.Systematic reviews · 2026Article
- Computer-assisted screening in systematic evidence synthesis requires robust and well-evaluated stopping criteria.Systematic reviews · 2024Article
- An exploration of available methods and tools to improve the efficiency of systematic review production: a scoping review.BMC medical research methodology · 2024Article
- Machine Learning Methods for Systematic Reviews:: A Rapid Scoping Review.Delaware journal of public health · 2023Article
- An active learning-based approach for screening scholarly articles about the origins of SARS-CoV-2.PloS one · 2022Article
- Statistical stopping criteria for automated screening in systematic reviews.Systematic reviews · 2020Article
- Mental health research in Botswana: a semi-systematic scoping review.The Journal of international medical research · 2020Article
- Predictive article recommendation using natural language processing and machine learning to support evidence updates in domain-specific knowledge graphs.JAMIA open · 2020Article
- SWIFT-Active Screener: Accelerated document screening through active learning and integrated recall estimation.Environment international · 2020Article
- Novel text analytics approach to identify relevant literature for human health risk assessments: A pilot study with health effects of in utero exposures.Environment international · 2020Review
- Improving reference prioritisation with PICO recognition.BMC medical informatics and decision making · 2019Article
- Prioritising references for systematic reviews with RobotAnalyst: A user study.Research synthesis methods · 2018Article
- A pilot validation study of crowdsourcing systematic reviews: update of a searchable database of pediatric clinical trials of high-dose vitamin D.Translational pediatrics · 2017Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
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).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.