Hierarchical Clustering of Multi-Study Depression Data Yields Four Symptom Clusters
Abstract
The Hamilton Rating Scale for Depression (HAM-D) evaluates depression severity based on 17 symptoms of major depressive disorder (MDD). Understanding the clustering of symptoms within MDD is helpful for the identification of depression subgroups that might differ in how they respond to various treatment interventions. In the research reported in this paper, we employed hierarchical clustering to generate a symptom clustering hierarchy. We compare our findings with previously identified factor analytic HAM-D symptom groups. Our findings are comparatively assessed with previously identified HAM-D symptom groups using factor analysis.
Authors: Lu Wang, Mark Chignell, Haoyan Jiang, Sachinthya Lokuge, Geneva E. Mason, Kathryn Fotinos, Martin A. Katzman
Published in: IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2021)