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Home-Based Multi-parameter Analysis for Early Risk Detection and Management of a Chronic Disease

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 869)

Abstract

Proactive support of patients with chronic diseases such as Congestive Heart Failure is vital since the recovery from a critical condition usually presents complications and it is not always possible. Although emergency situations may occur without prior warning, still in the majority of emergency cases, there are “signals” that precede their appearance. By capitalizing on technology developments that are changing the way how healthcare services are provided, we propose a multi-parameter and multi-level data analysis approach in order to detect possible alarms which can then trigger proper preventive medical interventions. The main contribution of the presented approach is a methodology that combines selected health parameters that can be measured in a home environment using ambient assisted living technologies, with clinical history, in order to design a risk detection system for a chronic disease based on a Bayesian reasoning network. The added value of the proposed approach is that the system not only collects, processes and transmits vital measurements to the healthcare experts but also detects risks within the collected data. The system developed is discussed in detail as well as the validation process performed both on a technical and a medical level.

Keywords

Risk prevention Bayesian network Chronic diseases Medical knowledge modelling Remote healthcare Multi-layered architecture Sensors Pervasive computing Ambient assisted living 

Notes

Acknowledgements

Part of this research has been co-financed by the European Union (European Social Fund – ESF) and Greek national funds through the Operational Program “DEPIN” of the National Strategic Reference Framework (NSRF) (Project code: 465435). The authors wish to thank the medical experts for their valuable contribution in this study, especially in the BN model validation process.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Information and Communication Systems Engineering DepartmentUniversity of the AegeanSamosGreece
  2. 2.Dynamic Ambient Intelligent Systems UnitComputer Technology Institute and Press DiophantusPatrasGreece
  3. 3.School of Science and TechnologyHellenic Open UniversityPatrasGreece

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