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Updated: Jun 24, 2025
11:21
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
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Optimized continuous homecare provisioning through distributed data-driven semantic services and cross-organizational
Mathias De Brouwer1, Pieter Bonte2, Dörthe Arndt3
1Department of Information Technology, IDLab - Ghent University - imec, 9052, Ghent, Belgium. Mathias.DeBrouwer@UGent.be.
Journal of Biomedical Semantics
|June 6, 2024
Summary
A new architecture using Semantic Web technologies optimizes healthcare coordination for better continuous care. This approach enhances data sharing and personalized services in homecare settings.
Area of Science:
- Computer Science
- Health Informatics
- Artificial Intelligence
Background:
- Increasing collaboration among healthcare providers necessitates efficient data and workflow coordination, especially with the shift to homecare.
- Machine-interpretable and reusable data exposure is crucial for optimal patient care.
- There is a need for smart, dynamic, personalized, and performant services with flexible workflows that meet quality constraints.
Purpose of the Study:
- To present a distributed, generic, cascading reasoning reference architecture for healthcare data and workflow coordination.
- To demonstrate the use of Semantic Web technologies for data-driven semantic services and cross-organizational workflows.
- To evaluate the performance and usability of the proposed architecture in a realistic homecare scenario.
Main Methods:
- A reference architecture instantiated with Semantic Web tools: RMLStreamer for Linked Data generation, DIVIDE for adaptive querying, Streaming MASSIF for service deployment, and AMADEUS for workflow composition.
- Configuration of rules using RMLEditor and Matey for Linked Data generation.
- Development of a use case demonstrator for personalized smart monitoring and cross-organizational treatment planning.
Main Results:
- The monitoring pipeline efficiently processes 14 observations per second.
- Key operations like mapping JSON to RDF (13.5 ms), fever alarm generation (26.4 ms), and smart notification (1539.5 ms) showed good performance.
- Treatment plan construction and conflict detection were completed in 190.8 ms and 1335.7 ms, respectively, demonstrating feasibility.
Conclusions:
- Existing Semantic Web technologies can be effectively leveraged to optimize continuous healthcare provisioning.
- The evaluation confirms the applicability, usability, and good performance of the architecture's components in a homecare setting.
- Further development of user interfaces is recommended to enhance tool adoption and integration.
Keywords:
Continuous homecareCross-organizational workflowsData-driven serviceDistributed architectureHealthcareStream reasoningMore Related Videos
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