Behavioral Predictive Analytics Towards Personalization for Self-management: a Use Case on Linking Health-Related Social Needs

• Published in: SN computer science Vol. 3; no. 3; p. 237
• Main Authors: Sy, Bon, Wassil, Michael, Connelly, Helene, Hassan, Alisha
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.05.2022; Springer Nature B.V
• Subjects: Attitudes; Biomedical Engineering Systems and Technologies; Chronic illnesses; Clustering; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Cost analysis; Customization; Data Structures and Information Theory; Diabetes; Dirichlet problem; Disease management; Exercise; Feasibility; Health behavior; Information Systems and Communication Service; Intervention; Literature reviews; Manifolds; Mathematical analysis; Motivation; Optimization; Organizational change; Original Research; Pattern Recognition and Graphics; Perceptions; Predictive analytics; Regression models; Software Engineering/Programming and Operating Systems; Theory of planned behavior; Vision; Latent dirichlet allocation; Self-health management; Natural language processing (NLP); Behavioral predictive analytics
• ISSN: 2662-995X; 2661-8907; 2661-8907
• DOI: 10.1007/s42979-022-01092-2

The objective of this research is to investigate the feasibility of applying behavioral predictive analytics to optimize patient engagement in diabetes self-management, and to gain insights on the potential of infusing a chatbot with NLP technology for discovering health-related social needs. In the U.S., less than 25% of patients actively engage in self-health management, even though self-health management has been reported to associate with improved health outcomes and reduced healthcare costs. The proposed behavioral predictive analytics relies on manifold clustering to identify subpopulations segmented by behavior readiness characteristics that exhibit non-linear properties. For each subpopulation, an individualized auto-regression model and a population-based model were 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. In addition to actionable self-health management, this research also investigates the feasibility of detecting health-related social needs through unstructured conversational dialog. 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, and the preliminary results on applying Latent Dirichlet Allocation to the conversational dialog of ten subjects for discovering social needs in five areas: food security, health (insurance coverage), transportation, employment, and housing.