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

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Abstract

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.

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Acknowledgements

The authors are grateful to the reviewers for their suggestions leading to the improvement of this manuscript. This research is conducted under the support of U.S. NSF phase 2 grant 1831214. Jin Chen, Magdalen Beiting-Parrish, and Connor Brown contributed to part of the technical results that were published in HealthInf 2021. Christina Miller (now leads the Office of Public Health at Montgomery County PA) helped spearhead the research direction in health-related social services. Michael Van der Gaag leads the usability study of the mobile app used in this research. Dr. Catherine Benedict had advised on this research regarding patient self-efficacy. Dr. Adebola Orafidiya (MD) had helped this pilot team by sharing clinical best practices on recommending self-monitoring. This pilot team has also benefited from the discussions with Dr. Joseph Tibaldi (MD) and Caterina Trovato (CDE) on patient engagement.

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Authors and Affiliations

  1. Queens College/City University of NY, 65-30 Kissena Blvd, Queens, NY, 11367, USA

    Bon Sy

  2. Graduate Center/City University of NY, 365 5th Ave, New York, NY, 10016, USA

    Bon Sy

  3. SIPPA Solutions, 42-06A Bell Blvd, Queens, NY, 11361, USA

    Bon Sy, Michael Wassil & Helene Connelly

  4. School of Public Health, Hunter College/City University of NY, New York, NY, 10065, USA

    Alisha Hassan

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Correspondence to Bon Sy.

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This article is part of the topical collection “Biomedical Engineering Systems and Technologies” guest edited by Hugo Gamboa and Ana Fred.

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Sy, B., Wassil, M., Connelly, H. et al. Behavioral Predictive Analytics Towards Personalization for Self-management: a Use Case on Linking Health-Related Social Needs. SN COMPUT. SCI. 3, 237 (2022). https://doi.org/10.1007/s42979-022-01092-2

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