This project aims to conduct novel research in text mining and machine learning to transform the way in which evidence-based public health (EBPH) reviews are conducted.
The goals of the project are as follows:
- to develop new text mining unsupervised methods for deriving term similarities, based on distributional semantics, to produce meaningful and high quality document and label clusters to support screen while searching in EBPH reviews.
- to develop new seariation algorithms for ranking and visualising meaningful associations of multiple types, dynamically and iteratively.
- to evaluate these newly developed methods in EBPH reviews, based on implementation of a pilot, to ascertain the level of transformation in EBPH reviewing.