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Accepted for/Published in: JMIR Formative Research

Date Submitted: Oct 2, 2020
Date Accepted: Feb 12, 2021

The final, peer-reviewed published version of this preprint can be found here:

Using a Personal Health Library–Enabled mHealth Recommender System for Self-Management of Diabetes Among Underserved Populations: Use Case for Knowledge Graphs and Linked Data

Ammar N, Bailey JE, Davis RL, Shaban-Nejad A

Using a Personal Health Library–Enabled mHealth Recommender System for Self-Management of Diabetes Among Underserved Populations: Use Case for Knowledge Graphs and Linked Data

JMIR Form Res 2021;5(3):e24738

DOI: 10.2196/24738

PMID: 33724197

PMCID: 8075073

The Personal Health Library (PHL): Enabling an mHealth Recommender System for Self-Management of Diabetes among Underserved Population with Multiple Chronic Conditions

  • Nariman Ammar; 
  • James E Bailey; 
  • Robert L Davis; 
  • Arash Shaban-Nejad

ABSTRACT

Background:

Traditionally, health data management has been EMR-based and mostly handled by health care providers. Mechanisms are needed to give patients more control over their health conditions. Personal Health Libraries (PHLs) provide a single point of secure access to patients' digital health information that can help empower patients to make better-informed decisions about their health.

Objective:

This article reports our efforts on leveraging tools and methods from artificial intelligence and knowledge representation to construct a private, decentralized PHL that supports interoperability and, ultimately, true care integration. Also, it describes the technological infrastructures required to build Hybrid Recommendation Systems that query the PHL to deliver tailored push notification interventions focused on improving self-care behaviors in diabetic and cancer adults from underserved communities.

Methods:

For the construction and management of the PHL, we leverage several technological infrastructures, including the Social Linked Data (Solid) platform, which builds on the W3C protocol standard and vocabularies as well as the Linked Open Data Stack. Solid enriches the Linked Data stack with modern development tools including JavaScript-based frameworks (e.g., React), which makes both integration tasks using APIs and building Solid enabled applications a seamless experience.

Results:

To showcase the framework functionalities we present a prototype design and demonstrate the main features through two use case scenarios motivated both by requirements identified in the literature and by recommendations from Physicians from both Hematology and Preventive medicine fields at two children’s hospitals in Memphis, TN.

Conclusions:

The proposed platform incorporates social determinants of health (SDoH) and ODLs in addition to digital health information to provide insights for informing both therapeutic and preventive interventions in chronic disease management. The PHL helps patients and their caregivers take a central role in making decisions regarding their health and equips health care providers with informatics tools to support the collection and interpretation of the collected knowledge


 Citation

Please cite as:

Ammar N, Bailey JE, Davis RL, Shaban-Nejad A

Using a Personal Health Library–Enabled mHealth Recommender System for Self-Management of Diabetes Among Underserved Populations: Use Case for Knowledge Graphs and Linked Data

JMIR Form Res 2021;5(3):e24738

DOI: 10.2196/24738

PMID: 33724197

PMCID: 8075073

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