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 which often entails spending hundreds of hours reading irrelevant articles.
Our solution to this problem is a Web application that applies machine learning techniques to semi-automate the research article screening process. Users can upload research articles from existing databases and manually screen a small portion of these articles. The Research Screener system will then actively learn the most relevant articles for the study and present these to the researcher during the screening process. This semi-automated approach can allow for thousands to tens of thousands of articles to be screened in hours / days rather than months.
The Research Screener system has been validated in a research paper and is currently being used by researchers in closed beta trials.
Dr. Leo Ng | email@example.com
Dr. Kevin Chai | firstname.lastname@example.org
© Research Screener