Advertisement

Towards Pain-Fingerprinting: A Ubiquitous and Interoperable Clinical Decision Support System for Pain Assessment

Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 73)

Abstract

The subjectivity and the multidimensionality of pain raises several challenges in terms of its description, assessment and treatment. This study presents a Clinical Decision Support System for the pain condition based on the fusion of its different dimensions in order to produce an accurate and reliable assessment. The proposed system includes not only the value of the pain intensity but also several scores (e.g. regarding anxiety, or depression) obtained from the analysis of patients’ behaviour related with the posted messages on social networks, such as Facebook, or Twitter. This study aims to introduce the paradigm for the pain assessment based on patients’ behavioural scores to the detriment of it self-reporting of pain.

Keywords

Clinical decision support system Pain assessment Multidimensionality of pain Pain intensity Pain monitoring 

Notes

Acknowledgements

This work was supported by FCT project UID/EEA/50008/2013 (Este trabalho foi suportado pelo projecto FCT UID/EEA/50008/2013).

This article is based upon work from COST Action IC1303—AAPELE—Architectures, Algorithms and Protocols for Enhanced Living Environments and COST Action CA16226—SHELD-ON—Indoor living space improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology). More information in www.cost.eu.

Conflict of Interest Statement

No conflicts of interest.

References

  1. 1.
    Giordano, J., Abramson, K., Boswell, M.V.: Pain assessment: subjectivity, objectivity, and the use of neurotechnology. Pain Physician 13, 305–315 (2010)Google Scholar
  2. 2.
    Pombo, N., Garcia, N., Bousson, K., Spinsante, S., Chorbev, I.: Pain assessment—can it be done with a computerised system? A systematic review and meta-analysis. Int. J. Environ. Res. Public Health 13, 415 (2016).  https://doi.org/10.3390/ijerph13040415CrossRefGoogle Scholar
  3. 3.
    Ong, K.S., Seymour, R.A.: Pain measurement in humans. Surgeon 2, 15–27 (2004)CrossRefGoogle Scholar
  4. 4.
    Melzack, R., Casey, K.L.: Sensory, motivational, and central control determinants of pain: a new conceptual model. In: The Skin Senses, pp. 423–443 (1968)Google Scholar
  5. 5.
    Fernandez, E., Turk, D.C.: Sensory and affective components of pain: separation and synthesis. Psychol. Bull. 112, 205–217 (1992)CrossRefGoogle Scholar
  6. 6.
    Holroyd, K.A., Talbot, F., Holm, J.E., Pingel, J.D., Lake, A.E., Saper, J.R.: Assessing the dimensions of pain: a multitrait-multimethod evaluation of seven measures. Pain 67, 259–265 (1996)CrossRefGoogle Scholar
  7. 7.
    Kornbluth, I.D., Freedman, M.K., Holding, M.Y., Overton, E.A., Saulino, M.F.: Interventions in chronic pain management. 4. Monitoring progress and compliance in chronic pain management. Arch. Phys. Med. Rehabil. 89, S51–S55 (2008)CrossRefGoogle Scholar
  8. 8.
    Pombo, N., Araújo, P., Viana, J.: Knowledge discovery in clinical decision support systems for pain management: a systematic review. Artif. Intell. Med. 60, 1–11 (2014).  https://doi.org/10.1016/j.artmed.2013.11.005CrossRefGoogle Scholar
  9. 9.
    Pombo, N., Rebelo, P., Araújo, P., Viana, J.: Combining data imputation and statistics to design a clinical decision support system for post-operative pain monitoring. Procedia Comput. Sci. 64, 1018–1025 (2015).  https://doi.org/10.1016/j.procs.2015.08.621CrossRefGoogle Scholar
  10. 10.
    Nekolaichuk, C.L., Bruera, E., Spachynski, K., MacEachern, T., Hanson, J., Maguire, T.O.: A comparison of patient and proxy symptom assessments in advanced cancer patients. Palliat. Med. 13, 311–323 (1999).  https://doi.org/10.1191/026921699675854885CrossRefGoogle Scholar
  11. 11.
    Pautex, S., Berger, A., Chatelain, C., Herrmann, F., Zulian, G.B.: Symptom assessment in elderly cancer patients receiving palliative care. Crit. Rev. Oncol./Hematol. 47, 281–286 (2003)CrossRefGoogle Scholar
  12. 12.
    Axén, I., Bodin, L., Bergström, G., Halasz, L., Lange, F., Lövgren, P., et al.: Clustering patients on the basis of their individual course of low back pain over a six month period. BMC Musculoskelet. Disord. 12, 99 (2011).  https://doi.org/10.1186/1471-2474-12-99CrossRefGoogle Scholar
  13. 13.
    Axén, I., Bodin, L., Bergström, G., Halasz, L., Lange, F., Lövgren, P.W., et al.: The use of weekly text messaging over 6 months was a feasible method for monitoring the clinical course of low back pain in patients seeking chiropractic care. J. Clin. Epidemiol. 65, 454–461 (2012).  https://doi.org/10.1016/j.jclinepi.2011.07.012CrossRefGoogle Scholar
  14. 14.
    Johnson, C.: Measuring pain. Visual analog scale versus numeric pain scale: what is the difference? J. Chiropr. Med. 4, 43–44 (2005).  https://doi.org/10.1016/s0899-3467(07)60112-8CrossRefGoogle Scholar
  15. 15.
    Ljótsson, B., Falk, L., Vesterlund, A.W., Hedman, E., Lindfors, P., Rück, C., et al.: Internet-delivered exposure and mindfulness based therapy for irritable bowel syndrome—a randomized controlled trial. Behav. Res. Ther. 48, 531–539 (2010).  https://doi.org/10.1016/j.brat.2010.03.003CrossRefGoogle Scholar
  16. 16.
    Labus, J.S., Bolus, R., Chang, L., Wiklund, I., Naesdal, J., Mayer, E.A., et al.: The Visceral Sensitivity Index: development and validation of a gastrointestinal symptom-specific anxiety scale. Aliment. Pharmacol. Ther. 20, 89–97 (2004).  https://doi.org/10.1111/j.1365-2036.2004.02007.xCrossRefGoogle Scholar
  17. 17.
    Svanborg, P., Asberg, M.: A new self-rating scale for depression and anxiety states based on the Comprehensive Psychopathological Rating Scale. Acta Psychiatr. Scand. 89, 21–28 (1994)CrossRefGoogle Scholar
  18. 18.
    Sheehan, K.H., Sheehan, D.V.: Assessing treatment effects in clinical trials with the discan metric of the Sheehan Disability Scale. Int. Clin. Psychopharmacol. 23, 70–83 (2008)CrossRefGoogle Scholar
  19. 19.
    Marceau, L.D., Link, C.L., Smith, L.D., Carolan, S.J., Jamison, R.N.: In-clinic use of electronic pain diaries: barriers of implementation among pain physicians. J. Pain Symptom Manage. 40, 391–404 (2010).  https://doi.org/10.1016/j.jpainsymman.2009.12.021CrossRefGoogle Scholar
  20. 20.
    Cleeland, C.S., Ryan, K.M.: Pain assessment: global use of the Brief Pain Inventory. Ann. Acad. Med. Singapore 23, 129–138 (1994)Google Scholar
  21. 21.
    Crombez, G., Bijttebier, P., Eccleston, C., Mascagni, T., Mertens, G., Goubert, L., et al.: The child version of the pain catastrophizing scale (PCS-C): a preliminary validation. Pain 104, 639–646 (2003).  https://doi.org/10.1016/S0304-3959(03)00121-0CrossRefGoogle Scholar
  22. 22.
    Radloff, L.S.: The CES-D Scale. Appl. Psychol. Meas. 1, 385–401 (1977).  https://doi.org/10.1177/014662167700100306CrossRefGoogle Scholar
  23. 23.
    Fairbank, J.C., Pynsent, P.B.: The Oswestry Disability Index. Spine 25, 2940–2952 (2000)CrossRefGoogle Scholar
  24. 24.
    Pombo, N., Araújo, P., Viana, J., da Costa, M.D.: Evaluation of a ubiquitous and interoperable computerised system for remote monitoring of ambulatory post-operative pain: a randomised controlled trial. Technol. Health Care 22, 63–75 (2014).  https://doi.org/10.3233/THC-130774CrossRefGoogle Scholar
  25. 25.
    Ruehlman, L.S., Karoly, P., Enders, C.: A randomized controlled evaluation of an online chronic pain self management program. Pain 153, 319–330 (2012).  https://doi.org/10.1016/j.pain.2011.10.025CrossRefGoogle Scholar
  26. 26.
    Crawford, J.R., Henry, J.D.: The Depression Anxiety Stress Scales (DASS): normative data and latent structure in a large non-clinical sample. Br. J. Clin. Psychol. 42, 111–131 (2003)CrossRefGoogle Scholar
  27. 27.
    Ruehlman, L.S., Karoly, P., Newton, C., Aiken, L.S.: The development and preliminary validation of the profile of chronic pain: extended assessment battery. Pain 118, 380–389 (2005).  https://doi.org/10.1016/j.pain.2005.09.001CrossRefGoogle Scholar
  28. 28.
    Ruehlman, L.S., Karoly, P., Newton, C., Aiken, L.S.: The development and preliminary validation of a brief measure of chronic pain impact for use in the general population. Pain 113, 82–90 (2005).  https://doi.org/10.1016/j.pain.2004.09.037CrossRefGoogle Scholar
  29. 29.
    Dudani, S.A.: The distance-weighted k-nearest-neighbor rule. IEEE Trans. Syst. Man Cybern. SMC-6, 325–327 (1976).  https://doi.org/10.1109/tsmc.1976.5408784CrossRefGoogle Scholar
  30. 30.
    Larrañaga, P., Moral, S.: Probabilistic graphical models in artificial intelligence. Appl. Soft Comput. 11, 1511–1528 (2011).  https://doi.org/10.1016/j.asoc.2008.01.003CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Instituto de Telecomunicações, Universidade da Beira InteriorCovilhãPortugal
  2. 2.Universidade Lusófona de Humanidades e TecnologiasLisbonPortugal

Personalised recommendations