Machine learning for systematic reviews
2019 Curtin Innovation Awards (Teaching & Learning)
2019 Australian Physiotherapy Association Conference Pitchfest (People's Choice Award)
2018 inaugural EduGrowth LaunchPad Business Start EdTech business plan competition
More than 65,000 research students in Australian Universities have the task of finding research papers relevant to their project. Individuals spending hundreds of hours often reading irrelevant articles.
Our solution to this problem is a Web application that applies machine learning techniques to semi-automate research article screening. Users will upload and abstracts of research articles from existing databases to the Web app then manually screen a small portion of these articles. The Research Screener will actively learn which articles are relevant, screening between 2,000 to 100,000 articles in minutes rather than researchers spending months to identify articles that are deemed relevant or irrelevant.
The application has been validated in a research paper and is currently being used by researchers in closed beta trials.
© Research Screener