Interactive exploration of a global clinical network from a large breast cancer cohort.

ABSTRACT: Despite unprecedented amount of information now available in medical records, health data remain underexploited due to their heterogeneity and complexity. Simple charts and hypothesis-driven statistics can no longer apprehend the content of information-rich clinical data. There is, therefore, a clear need for powerful interactive visualization tools enabling medical practitioners to perceive the patterns and insights gained by state-of-the-art machine learning algorithms. Here, we report an interactive graphical interface for use as the front end of a machine learning causal inference server (MIIC), to facilitate the visualization and comprehension by clinicians of relationships between clinically relevant variables. The widespread use of such tools, facilitating the interactive exploratio

SUBMITTER: Sella N 

PROVIDER: S-EPMC9365762 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature