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Scalable real-time health data sensing and analysis enabling collaborative care delivery

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Abstract

This work describes a novel end-to-end data ingestion and runtime processing pipeline, which is a core part of a technical solution aiming to monitor frailty indices of patients during and after treatment and improve their quality of life. The focus of this work is on the technical architectural details and the functionalities provided, which have been developed in a manner that are extensible, scalable and fault-tolerant by design. Extensibility refers to both data sources and the exact specification of analysis techniques. Our platform can combine data not only from multiple sensor types but also from electronic health records. Also, the analysis component can process the patient data both individually and in combination with other patients, while exploiting both cloud and edge resources. We have shown concrete examples of advanced analytics and evaluated the scalability of the system, which has been fully prototyped.

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Availability of data and materials

Not applicable.

Code availability

The main DIRAP code in the form of preprepared Docker images, will become available upon the end of the LifeChamps project.

Notes

  1. https://ec.europa.eu/eurostat/.

  2. https://lifechamps.eu/.

  3. https://mqtt.org/.

  4. https://flink.apache.org/.

  5. https://kafka.apache.org/.

  6. https://opendistro.github.io/for-elasticsearch/.

  7. https://spring.io/projects/spring-boot.

  8. https://www.fitbit.com/global/uk/products/trackers/charge4.

  9. https://www.withings.com/us/en/body-plus.

  10. https://www.movesense.com/.

  11. https://www.openuv.io/.

  12. https://dev.fitbit.com/build/reference/web-api/.

  13. https://redis.io/.

  14. https://developer.withings.com/api-reference/#operation/dropshipmentv2-update.

  15. We mainly provide this functionality on the cloud, since Flink is notoriously good for low-latency streaming applications, however, we have also incorporated CEP functionality on the edge if required.

  16. https://ci.apache.org/projects/flink/flink-docs-release-1.13/docs/libs/cep/.

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Acknowledgements

This research work has been supported by the European Commission under the Horizon 2020 Programme, through funding of the LifeChamps project (Grant 875329).

Funding

This research work has been supported by the European Commission under the Horizon 2020 Programme, through funding of the LifeChamps project (Grant 875329).

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Author notes

  1. Ilias Dimitriadis, Ioannis Mavroudopoulos and Styliani Kyrama are contributed equally to this work.

Authors and Affiliations

  1. Department of Informatics, Aristotle University of Thessaloniki, Thessaloníki, Greece

    Ilias Dimitriadis, Ioannis Mavroudopoulos, Styliani Kyrama, Theodoros Toliopoulos, Anastasios Gounaris & Athena Vakali

  2. School of Medicine, Aristotle University of Thessaloniki, Thessaloníki, Greece

    Antonis Billis & Panagiotis Bamidis

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  1. Ilias Dimitriadis
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  2. Ioannis Mavroudopoulos
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Contributions

All authors have actively contributed to the design of the solution and the development of the material described in this work. The system implementation was done by the researchers I.D., I.M., S.K. and T.T.

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Correspondence to Ilias Dimitriadis.

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Dimitriadis, I., Mavroudopoulos, I., Kyrama, S. et al. Scalable real-time health data sensing and analysis enabling collaborative care delivery. Soc. Netw. Anal. Min. 12, 63 (2022). https://doi.org/10.1007/s13278-022-00891-y

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