  The exponential growth of scientific production makes secondary literature
abridgements increasingly demanding. We introduce a new open-source framework
for systematic reviews that significantly reduces time and workload for
collecting and screening scientific literature. The framework provides three
main tools: 1) an automatic citation search engine and manager that collects
records from multiple online sources with a unified query syntax, 2) a
Bayesian, active machine learning, citation screening tool based on iterative
human-machine interaction to increase predictive accuracy and, 3) a
semi-automatic, data-driven query generator to create new search queries from
existing citation data sets. To evaluate the automatic screener's performance,
we estimated the median posterior sensitivity and efficiency [90% Credible
Intervals] using Bayesian simulation to predict the distribution of undetected
potentially relevant records. Tested on an example topic, the framework
collected 17,755 unique records through the citation manager; 766 records
required human evaluation while the rest were excluded by the automatic
classifier; the theoretical efficiency was 95.6% [95.3%, 95.7%] with a
sensitivity of 100% [93.5%, 100%]. A new search query was generated from the
labelled dataset, and 82,579 additional records were collected; only 567
records required human review after automatic screening, and six additional
positive matches were found. The overall expected sensitivity decreased to
97.3% [73.8%, 100%] while the efficiency increased to 98.6% [98.2%, 98.7%]. The
framework can significantly reduce the workload required to conduct large
literature reviews by simplifying citation collection and screening while
demonstrating exceptional sensitivity. Such a tool can improve the
standardization and repeatability of systematic reviews.
