Unsupervised and Dynamic Dendrogram-Based Visualization of Medical Data

Visualizing the correlations between structured medical data in the form of Electronic health records (EHRs) is of major importance for effective and efficient medical data analysis and decision-making. This work describes an unsupervised semi-structured and feature-based tool for dynamic EHR data visualization called “mirrored dendrograms”. It accepts as input semi-structured EHRs, and allows the user to select the target features to be visualized and mapped against each other, and their relative weights on the visualization process. It then invokes a hierarchical clustering process to cluster the data following the user-chosen features, and produces a dendrogram structure for each combination of target features. The dendrograms are mirrored against each other by mapping their nodes using the transportation optimization problem, allowing the user to dynamically zoom-in and out of the mapping at different granularity levels. We have evaluated our solution using a sample dataset of 114 EHRs of patients who suffer from migraine disorder. A group of 20 testers participated in the evaluations to assess the tool compared with existing solutions. Results showcase the tool’s performance.