Measures of Cluster Informativeness for Medical Evidence Aggregation and Dissemination
This addresses the challenge of information access and extraction for researchers and clinicians in biomedicine, but it is incremental as it builds on existing clustering and visualization techniques.
The study tackled the problem of organizing and visualizing medical evidence in PubMed by developing and evaluating clustering methods for controlled vocabularies, resulting in downstream network visualizations that aid in discovering genetic and molecular associations.
The largest collection of medical evidence in the world is PubMed. However, the significant barrier in accessing and extracting information is information organization. A factor that contributes towards this barrier is managing medical controlled vocabularies that allow us to systematically and consistently organize, index, and search biomedical literature. Additionally, from users' perspective, to ultimately improve access, visualization is likely to play a powerful role. There is a strong link between information organization and information visualization, as many powerful visualizations depend on clustering methods. To improve visualization, therefore, one has to develop concrete and scalable measures for vocabularies used in indexing and their impact on document clustering. The focus of this study is on the development and evaluation of clustering methods. The paper concludes with demonstration of downstream network visualizations and their impact on discovering potentially valuable and latent genetic and molecular associations.