Measures of Cluster Informativeness for Medical Evidence Aggregation and Dissemination

TL;DR

This paper develops and evaluates clustering measures for medical vocabularies to enhance evidence organization and visualization, ultimately aiding in discovering hidden biomedical relationships.

Contribution

It introduces scalable measures for vocabularies that improve clustering quality and visualization in biomedical literature analysis.

Findings

Effective clustering measures enhance evidence organization.

Improved visualization reveals latent biomedical associations.

Demonstrated impact on genetic and molecular discovery.

Abstract

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.