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Population Health Status Assessment Using Large Scale Vital Signal Data Sets

  • Miklós KozlovszkyEmail author
Chapter
Part of the Topics in Intelligent Engineering and Informatics book series (TIEI, volume 14)

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.

Keywords

Remote patient monitoring Population health status assessment Health disk 

Notes

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.

References

  1. 1.
    H.B. Mitchell, Data Fusion: Concepts and Ideas, 2nd edn. (Springer, Heidelberg, 2014). ISBN 978-3-642-43730-4Google Scholar
  2. 2.
    B.V. Dasarathy, Decision Fusion (IEEE Computer Society Press, 1994)Google Scholar
  3. 3.
    P. Bakucz, S. Willems, B.A. Hoffmann, Universal fluctuations in very short ECG episodes. Acta Polytech. Hung. 11(7), 10 (2014)Google Scholar
  4. 4.
    C-M Lin, Y-J Mon, C-H Lee, J-G Juang, I.J. Rudas, ANFIS-based indoor location awareness system for the position monitoring of patients. Acta Polytech. Hung. 11(1), 37–48 (2014)Google Scholar
  5. 5.
    M.H. Ullman-Culleré, C.J. Foltz, Body Condition Scoring: A Rapid and Accurate Method for Assessing Health Status in Mice, vol. 49, no. 3 (Laboratory Animal Science, Copyright 1999, by the American Association for Laboratory Animal Science, 1999)Google Scholar
  6. 6.
    A.C. Beynen, V. Baumans, A.P.M.G. Bertens et al., Assessment of discomfort in gallstone-bearing mice: a practical example of the problems encountered in an attempt to recognize discomfort in laboratory animals. Lab. Anim. 21, 35–42 (1987)CrossRefGoogle Scholar
  7. 7.
    D.B. Morton, P.H.M. Griffiths, Guidelines on the recognition of pain and discomfort in experimental animals and an hypothesis for assessment. Vet. Rec. 116, 431–436 (1985)CrossRefGoogle Scholar
  8. 8.
    P. Workman, A. Balmain, J.A. Hickman et al., UKCCCR guidelines for the welfare of animals in experimental neoplasia. Lab. Anim. 22, 195–201 (1988)CrossRefGoogle Scholar
  9. 9.
    E.S. Redgate, M. Deutsch, S.S. Boggs, Time of death of CNS tumor-bearing rats can be reliably predicted by body weight-loss patterns. Lab. Anim. Sci. 41, 269–273 (1991)Google Scholar
  10. 10.
    J.J. Domecq, A.L. Skidmore, J.W. Lloyd et al., Validation of body condition scoring with ultrasound measurements of subcutaneous fat in dairy cows. J. Dairy Sci. 78, 2308–2313 (1995)CrossRefGoogle Scholar
  11. 11.
    J.D. Ferguson, D.T. Galligan, N. Thomsen, Principal descriptors of body condition score in Holstein cows. J. Dairy Sci. 77, 2695–2703 (1994)CrossRefGoogle Scholar
  12. 12.
    K.H. Cooper, Aerobics (Bantam Books, 1968). ISBN 978-0-553-14490-1Google Scholar
  13. 13.
    U. Siebert, O. Alagoz, A.M. Bayoumi, J. Beate, D.K. Owens, D.J. Cohen, K.M. Kuntz, State-transition modeling a report of the ISPOR-SMDM modeling good research practices task force-3. Med. Decis. Making 32(5), 690–700 (2012).  https://doi.org/10.1177/0272989x12455463
  14. 14.
    E. Toth-Laufer, M. Takacs, Risk level calculation for body physical exercise with different fuzzy based methods, in 2011 IEEE 12th International Symposium on Computational Intelligence and Informatics (CINTI), pp. 583–586, Print ISBN: 978-1-4577-0044-6, INSPEC Accession Number: 12442577,  https://doi.org/10.1109/cinti.2011.6108469
  15. 15.
    Y. Wu, Y. Ding, H. Xu, Comprehensive fuzzy evaluation model for body physical exercise, in Risk Life System Modeling and Simulation. Lecture Notes in Computer Science, vol. 4689/2007 (2007), pp. 227–235.  https://doi.org/10.1007/978-3-540-74771-0_26
  16. 16.
    B.D. Brown, F. Badilini, HL7 Version 3 implementation guide: regulated studies—annotated ECG, release 1. Health Lev. Seven Int. (2005)Google Scholar
  17. 17.
    J.J. Pretto, T. Roebuck, L. Beckert, G. Hamilton, Clinical use of pulse oximetry: official guidelines from the Thoracic Society of Australia and New Zealand. Clin. Pract. Guid. Respirol. 19, 38–46 (2014).  https://doi.org/10.1111/resp.12204
  18. 18.
    M. Kozlovszky, Multi-parameter health state assessment, in 2015 4th International Work Conference on Bioinspired Intelligence (IWOBI), San Sebastian (2015), pp. 145–150.  https://doi.org/10.1109/iwobi.2015.7160158
  19. 19.
    M. Kozlovszky, K. Batbayar, Z. Garaguly, K. Karózkai, Multimodal biophysical data visualization for patient monitoring, in 2016 IEEE 11th International Symposium on Applied Computational Intelligence and Informatics (SACI), Timisoara (2016), pp. 401–406.  https://doi.org/10.1109/saci.2016.7507411

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Biotech Research Center, EKIKÓbuda UniversityBudapestHungary

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