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Behavioral Predictive Analytics Towards Personalization for Self-management: a Use Case on Linking Health-Related
Bon Sy1,2,3, Michael Wassil3, Helene Connelly3
1Queens College/City University of NY, 65-30 Kissena Blvd, Queens, NY 11367 USA.
SN Computer Science
|May 2, 2022
Summary
This study explores using behavioral analytics and AI chatbots to improve diabetes self-management and identify social needs. Personalized strategies boost patient engagement, while NLP uncovers critical social determinants of health.
Area of Science:
- Digital Health
- Behavioral Science
- Artificial Intelligence
Background:
- Low patient engagement in diabetes self-management (less than 25% in the U.S.) is linked to poorer health outcomes and higher costs.
- Personalized interventions are crucial for improving adherence to self-management behaviors like glucose monitoring, diet, and exercise.
Purpose of the Study:
- To assess the feasibility of behavioral predictive analytics for optimizing patient engagement in diabetes self-management.
- To explore the potential of Natural Language Processing (NLP) chatbots in identifying patients' health-related social needs.
Main Methods:
- Behavioral predictive analytics utilizing manifold clustering to segment patient subpopulations based on readiness characteristics.
- Development of individualized auto-regression and population-based models for personalized self-management recommendations.
- Application of Latent Dirichlet Allocation (LDA) to analyze conversational data for social needs detection.
Main Results:
- Identified distinct subpopulations through manifold clustering of 148 type 2 diabetes subjects.
- Demonstrated preliminary personalized engagement strategies for 22 subjects across various scenarios.
- Preliminary results from LDA analysis on 10 subjects identified social needs in food security, health insurance, transportation, employment, and housing.
Conclusions:
- Behavioral predictive analytics shows promise for personalizing diabetes self-management and enhancing patient engagement.
- NLP-powered chatbots offer a feasible approach for uncovering social needs that impact health management.
- Integrating predictive analytics and conversational AI can lead to more holistic and effective patient care strategies.
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