Digital Phenotype Representation by Statistical, Information Theory, Data-Driven Approach with Digital Health Data
Binh P. Nguyen, Michael Nigro, Alice Rueda, Venkat Bhat, Sridhar Krishnan
Abstract
Digital phenotyping (DP) is a multidisciplinary field of science that quantifies the individual level phenotype through active and passive data. Although DP is a multidisciplinary field, there lacks a technical and a systematic approach to representing DP. This work proposes the development of digital phenotype profile (DPP) to represent a user’s physical and behavioural health baseline through systematic investigations with an emphasis on robustness and explainability. To achieve this, a Statistical, Information Theory, and Data-driven (SID) pipeline will develop the foundation of the DPP. SID evaluates the non-linearity of the signal to offer inference for domain-specific feature extraction, evaluates the information theory to rank the DPP parameters, and imputes missing data for robust analysis, respectively. SID was applied to a 24-hr Multi-Level dataset and was able to represent individual DPPs. The respective DPPs were visualized and clusters of awake and asleep were used for individual specific modelling.
BibTeX
@inproceedings{icassp2023_digitalphenotype,
title = {Digital Phenotype Representation by Statistical, Information Theory, Data-Driven Approach with Digital Health Data},
author = {Binh P. Nguyen and Michael Nigro and Alice Rueda and Venkat Bhat and Sridhar Krishnan},
booktitle = {ICASSP 2023},
year = {2023}
}