Kokiri: Random-Forest-Based Comparison and Characterization of Cohorts @ IEEE VIS 2022
Channel: JKU Visual Data Science Lab
Date: 2022-10-17

Pre-recorded Talk by Klaus Eckelt for IEEE VIS Workshop on Visualization in Biomedical AI 2022.

For slides and more information, visit: https://jku-vds-lab.at/publications/2022_kokiri/

We propose an interactive visual analytics approach to characterizing and comparing patient subgroups (i.e., cohorts). Despite having the same disease and similar demographic characteristics, patients respond differently to therapy. One reason for this is the vast number of variables in the genome that influence a patient's outcome. Nevertheless, most existing tools do not offer effective means of identifying the attributes that differ most, or look at them in isolation and thus ignore combinatorial effects. To fill this gap, we present Kokiri, a visual analytics approach that aims to separate cohorts based on user-selected data, ranks attributes by their importance in distinguishing between cohorts, and visualizes cohort overlaps and separability. With our approach, users can additionally characterize the homogeneity and outliers of a cohort. To demonstrate the applicability of our approach, we integrated Kokiri into the Coral cohort analysis tool to compare and characterize lung cancer patient cohorts.