0000000000621623
AUTHOR
Veronica Distefano
Trajectory-based and Sound-based Medical Data Clustering
Challenges in medicine are often faced as interdisciplinary endeav- ors. In such an interdisciplinary view, sonification of medical data provides an additional sensory dimension to highlight often hard- to-find information and details. Some examples of sonification of medical data include Covid genome mapping [5], auditory repre- sentations of tridimensional objects as the brain [4], enhancement of medical imagery through the use of sound [1]. Here, we focus on kidney filtering-efficiency time-evolution data. We consider the estimated glomerular filtration rate (eGFR), the main indicator of kidney efficiency in diabetic kidney disease patients.1 We propose a technique to sonify the eGFR tra…
Classes of Colors and Timbres: A Clustering Approach
Similarities between different sensory dimensions can be addressed considering common “movements” as causes, and emotional responses as effects. An imaginary movement toward the “dark” produces “dark sounds” and “dark colors,” or, toward the “bright,” “brighter colors” and “brighter sounds.” Following this line of research, we draw upon the confluence of mathematics and cognition, extending to colors and timbres the gestural similarity conjecture, a development of the mathematical theory of musical gestures. Visual “gestures” are seen here as paths in the space of colors, compared with paths in the space of orchestral timbres. We present an approach based on clustering algorithm to evaluate…
Exploring Heterogeneity with Category and Cluster Analyses for Mixed Data
Precision medicine aims to overcome the traditional one-model-fits-the-whole-population approach that is unable to detect heterogeneous disease patterns and make accurate personalized predictions. Heterogeneity is particularly relevant for patients with complications of type 2 diabetes, including diabetic kidney disease (DKD). We focus on a DKD longitudinal dataset, aiming to find specific subgroups of patients with characteristics that have a close response to the therapeutic treatment. We develop an approach based on some particular concepts of category theory and cluster analysis to explore individualized modelings and achieving insights onto disease evolution. This paper exploits the vi…