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.