Behavioral Predictive Analytics towards Personalization for Self-management

Bon Sy, Bon Sy, Bon Sy, Jin Chen, Magdalen Beiting-Parrish, Magdalen Beiting-Parrish, Connor Brown

2021

Abstract

The objective of this research is to investigate the feasibility of applying behavioral predictive analytics to optimize diabetes self-management. In the U.S., less than 25% of patients actively engage in self-management even though self-management has been reported to associate with improved health outcomes and reduced healthcare costs. The proposed behavioral predictive analytics relies on manifold clustering to derive nonlinear clusters. These clusters are characterized by behavior readiness patterns for subpopulation segmentation. For each subpopulation, an individualized auto-regression model and a population-based model are developed to support self-management personalization in three areas: glucose self-monitoring, diet management, and exercise. The goal is to predict personalized activities that are most likely to achieve optimal engagement. This paper reports the result of manifold clusters based on 148 subjects with type 2 diabetes, and shows the preliminary result of personalization for 22 subjects under different scenarios.

Paper Citation

in Harvard Style

Sy B., Chen J., Beiting-Parrish M. and Brown C. (2021). Behavioral Predictive Analytics towards Personalization for Self-management. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF; ISBN 978-989-758-490-9, SciTePress, pages 111-121. DOI: 10.5220/0010231801110121

in Bibtex Style

@conference{healthinf21,
author={Bon Sy and Jin Chen and Magdalen Beiting-Parrish and Connor Brown},
title={Behavioral Predictive Analytics towards Personalization for Self-management},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF},
year={2021},
pages={111-121},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010231801110121},
isbn={978-989-758-490-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF
TI - Behavioral Predictive Analytics towards Personalization for Self-management
SN - 978-989-758-490-9
AU - Sy B.
AU - Chen J.
AU - Beiting-Parrish M.
AU - Brown C.
PY - 2021
SP - 111
EP - 121
DO - 10.5220/0010231801110121
PB - SciTePress