Web App

Rapid Systematic Literature Review Prototype (2020)

To create an automated process to help CDC’s Covid Mitigation team analyze and categorize legislation documents more rapidly. The current process is entirely manual, and resulted in a backlog of thousands of unprocessed documents.


The R&D team created the Rapid Review web application to help accelerate the process of targeted Systematic Reviews. The Rapid Review system enables a legal analyst to quickly narrow down a collection of Titles and Abstracts from a literature review search to those that are most relevant to the given question. The final process was shown to eliminate 60-70% of human effort, while maintaining a high degree (80-90%) of accuracy compared to a fully manual process.


The Rapid Review algorithm was tested using python’s scikit-learn framework for Machine Learning. After testing several architectural options, the team refined an algorithm which replicated the dual-reviewer of traditional systematic reviews, leveraging an ensemble of different machine learning (ML) models to help identify the most difficult documents to classify.


Rather than building a system that classifies the documents directly, the Rapid Review system leverages the principles of active learning. In this type of process, a human makes all document classifications, with the ML model acting as a recommendation system that provides only those documents with a high probability of relevance to the research question.


The algorithm was bundled into a flask-based API and hosted in a Docker container. In addition, a custom front-end app (React/Node.js) was developed for a User Interface. The interface provided a simple portal for user authentication, document tagging, and project management/organization.



Office of the Associate Director for Policy and Strategy (OADPS)


  • Single page web app development
  • User Experience Design
  • Responsive Web Design/Development
  • Python
  • Scikit-learn
  • Flask RESTful API
  • ReactJS
  • MaterialUI
  • Docker