Abstract
With the advent of the recent remote patient monitoring solutions, biosignal acquisition using wearable sensors can enable national healthcare systems to monitor vast amount of various vital signs/life signals in near real-time from large population easily. Population health status assessment is able to provide insight information about the population at large scale. Population health record values can be calculated from the individual patient health records and patient health states. The large number of data sources multiplied by the number of sensor modalities, with high sampling rates produce big data problem. We have developed a method to deal with this huge amount of data. In this paper we are providing information about our developed health status assessment framework, which supports population level health status assessment, and also applicable to use at patient level.
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Acknowledgements
The author hereby thank the GINOP-2.2.1-15-2017-00073 “Telemedicina alapú ellátási formák fenntartható megvalósítását támogató keretrendszer kialakítása és tesztelése” project and furthermore the University Innovation and Research Center—Obuda University, Hungary (EKIK) for their financial support.
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Kozlovszky, M. (2020). Population Health Status Assessment Using Large Scale Vital Signal Data Sets. In: Kovács, L., Haidegger, T., Szakál, A. (eds) Recent Advances in Intelligent Engineering. Topics in Intelligent Engineering and Informatics, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-030-14350-3_14
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DOI: https://doi.org/10.1007/978-3-030-14350-3_14
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