Model-Based Clustering of Multiple Two-Dimensional Functional Data Incorporating Covariates

• Activity Number: 25 - Medical Devices and Diagnostics Speed Session
• Type: Contributed
• Date/Time: Sunday, August 8, 2021 : 1:30 PM to 3:20 PM
• Sponsor: Section on Medical Devices and Diagnostics
• Author(s): Ying Cui* and Jeong Hoon Jang and Amita K. Manatunga
• Companies: Emory University and Indiana University and Emory University
• Keywords: Radial cubic B-splines; Two-dimensional functional data; Clustering method; Functional data with covariates

Abstract

This work is motivated by the need to develop non-invasive tools for screening anemia and reduce phlebotomy burden in patients. Specifically, we introduce a new clustering method that can non-invasively separate patients into low and high anemia risk groups using clinical pallor data extracted from patient sourced fingernail photos. To increase efficiency and accuracy of clustering, we propose a novel clustering algorithm based on a latent class functional mixed model. An EM algorithm is derived to estimate the model parameters and latent cluster memberships. We further introduce a data-driven approach for choosing the appropriate number of clusters based on the "distortion function" adapted to our setting. Our simulation study demonstrates that the proposed method outperforms other competing methods for clustering two-dimensional functional data with and without covariate information. The proposed method is applied to cluster patient-sourced fingernail photos collected at the Emory University Hospital, which unveils useful subpopulation structures of fingernail photos whose relationships with the underlying physiological mechanism of anemia can be further delineated.