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Published on: December 11, 2016
Toward modernizing the systematic review pipeline in genetics: efficient updating via data mining
Byron C Wallace1, Kevin Small, Carla E Brodley
1Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA. byron.wallace@gmail.com
Summary
Modern data mining tools significantly reduce the labor required for systematic reviews. These tools achieve high accuracy in identifying relevant citations, minimizing the need for extensive human screening during updates.
Area of Science:
- Biomedical Informatics
- Evidence Synthesis
- Health Services Research
Background:
- Systematic reviews are crucial for evidence-based healthcare but are labor-intensive to produce and maintain.
- Updating systematic reviews requires continuous screening of new literature, posing a significant burden on researchers.
Purpose of the Study:
- To demonstrate the utility of data mining tools in reducing the manual effort involved in updating systematic reviews.
- To evaluate the performance of classification models in identifying relevant citations for systematic review maintenance.
Main Methods:
- Utilized four continuously updated, manually curated databases (PDGene, AlzGene, SzGene, and Tufts CEA Registry).
- Trained classification models on citations screened up to 2009.
- Evaluated model performance in classifying 2010 citations against human screening as the gold standard.
Main Results:
- Classification models achieved 100% sensitivity for three databases and 99% for one, missing no eligible citations.
- Specificities ranged from 73% to 93%.
- Semiautomated systems could reduce human screening workload by 67% to 89%.
Conclusions:
- Data mining methodologies effectively reduce the burden of updating systematic reviews.
- These tools maintain high accuracy, comparable to human screening, without missing additional relevant papers.
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