Skip to main content

Advertisement

Log in

Fuzzy regression model clustering algorithm based assessment, analysis and acceptance of palliative care and psychological behaviors of elderly patients with cancer pain

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

To investigate the effect of palliative care on the psychological status of patients with advanced cancer. 106 elderly patients with cancer pain were randomly divided into observation group and control group, before enrollment, the symptoms check list was filled to assess the patient’s mental health level; the control group received conventional cancer nursing intervention, the observation group received palliative care intervention in addition to conventional nursing. 2 weeks after nursing intervention, The symptom check list(SCL-90) and Functional Assessment of Cancer Therapy-General(FACT-G) were released in the same way and the two groups of patients were re-evaluated. Symptom check list of the two groups before enrollment was higher than that of the national norm; after 2 weeks of nursing intervention, the score of symptom check list in the observation group was lower than that in the control group. The total scores total number of positive symptoms and the mean was lower than that before the intervention, with significant difference. Palliative care can effectively improve the advanced cancer patients somatization, coercion, interpersonal sensitivity, anxiety, depression and other adverse psychological conditions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Reference

  1. Duggleby, W.: Elderly hospice cancer patients’ descriptions of their pain experiences. Am. J. Hosp. Palliat. Care 17(2), 111–117 (2000)

    Google Scholar 

  2. Hui, D., De, l.C.M, Thorney, S., et al.: The frequency and correlates of spiritual distress among patients with advanced cancer admitted to an acute palliative care unit. Am. J. Hosp. Palliat. Care 28(4), 264 (2011)

    Google Scholar 

  3. Patel, M.I., Bhattacharya, J., Asch, S.M., et al.: Acceptance of advance directives and palliative care referral for veterans with advanced cancer: a retrospective analysis. Am. J. Hosp. Palliat. Care 33(8), 742 (2016)

    Google Scholar 

  4. Seyedfatemi, N., Borimnejad, L., Mardani, H.M., et al.: Iranian nurses’ perceptions of palliative care for patients with cancer pain. Int. J. Palliat. Nurs. 20(2), 69 (2014)

    Google Scholar 

  5. De, W.K.: Palliative care problems in the elderly patients. Prz. Lek. 59(4–5), 355 (2002)

    Google Scholar 

  6. Thompson, G.N., Chochinov, H.M., Wilson, K.G., et al.: Prognostic acceptance and the well-being of patients receiving palliative care for cancer. J. Clin. Oncol. 27(34), 5757 (2009)

    Google Scholar 

  7. Dionneodom, J.N., Azuero, A., Lyons, K.D., et al.: Benefits of early versus delayed palliative care to informal family caregivers of patients with advanced cancer: outcomes from the ENABLE III randomized controlled trial. J. Clin. Oncol. 33(13), 1446–1452 (2015)

    Google Scholar 

  8. Dawber, R., Armour, K., Ferry, P., et al.: Comparison of informal caregiver and named nurse assessment of symptoms in elderly patients dying in hospital using the palliative outcome scale. Bmj Support. Palliat. Care (2016). https://doi.org/10.1136/bmjspcare-2015-000850

    Google Scholar 

  9. Kim, M., Cho, C., Lee, C.: A concept analysis of quality of dying and death (QODD) for non-cancer patients:from the perspective of palliative care. Asian J. Hum. Serv. 9, 96–106 (2015)

    Google Scholar 

  10. Burton, A.W.: What every professional working in palliative care should know about cancer pain management. Psicooncol. Investig. Y Clín. Biopsicosoc. En Oncol 1, 57–90 (2004)

    Google Scholar 

  11. Davis, E.L., Deane, F.P., Gcb, L., et al.: Is higher acceptance associated with less anticipatory grief among patients in palliative care? J. Pain Symp. Manag. 54(1), 120 (2017)

    Google Scholar 

  12. Zhang, Y., Algburi, A., Wang, N., Kholodovych, V., Oh, D.O., Chikindas, M., Uhrich, K.E.: Self-assembled cationic amphiphiles as antimicrobial peptides mimics: Role of hydrophobicity, linkage type, and assembly state. Nanomedicine 13(2), 343–352 (2017)

    Google Scholar 

  13. Arunkumar, N., Kumar, K.R., Venkataraman, V.: Automatic detection of epileptic seizures using new entropy measures. J. Med. Imag. Health Inf. 6(3), 724–730 (2016)

    Google Scholar 

  14. Hamza, R., Muhammad, K., Nachiappan, A., González, G.R.: Hash based encryption for keyframes of diagnostic hysteroscopy. IEEE Access (2017). https://doi.org/10.1109/ACCESS.2017.2762405

    Google Scholar 

  15. Abdelhamid, D.S., Zhang, Y., Lewis, D.R., Moghe, P.V., Welsh, W.J., Uhrich, K.E.: Tartaric acid-based amphiphilic macromolecules with ether linkages exhibit enhanced repression of oxidized low density lipoprotein uptake. Biomaterials 53, 32–39 (2015)

    Google Scholar 

  16. Arunkumar, N., Ramkumar, K., Hema, S., Nithya, A., Prakash, P., Kirthika, V.: Fuzzy Lyapunov exponent based onset detection of the epileptic seizures. In: Proceedings of 2013 IEEE Conference on Information and Communication Technologies, ICT 2013, art. no. 6558185, pp. 701–706. (2013)

  17. Chen, X., Pang, L., Guo, P., Sun, X., Xue, Z., Arunkumar, N.: New upper degree of freedom in transmission system based on wireless G-MIMO communication channel. Clust. Comput. (2017). https://doi.org/10.1007/s10586-017-1513-0

    Google Scholar 

  18. Fernandes, S.L., Gurupur, V.P., Sunder, N.R., Arunkumar, N., Kadry, S.: A novel nonintrusive decision support approach for heart rate measurement. Pattern Recognit. Lett. (2017). https://doi.org/10.1016/j.patrec.2017.07.002

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ke Xi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xi, K., Huiyu, L., Xiaohua, H. et al. Fuzzy regression model clustering algorithm based assessment, analysis and acceptance of palliative care and psychological behaviors of elderly patients with cancer pain. Cluster Comput 22 (Suppl 2), 4813–4820 (2019). https://doi.org/10.1007/s10586-018-2391-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2391-9

Keywords

Navigation