Abstract
In the patient treatment process, many aspects should be addressed – among all the patients’ psychological profiles. Individual approaches to each patient can be unrealizable due to various limitations in a public healthcare system. We propose the patients’ segmentation method based on the standardized Coping Inventory for Stressful Situations (CISS) instrument and clustering algorithm that finds similarities of patients and enables the application of tailored interventions directed and adjusted to specifics of identified segments of patients. We carried out two clustering experiments using k-means clustering with automated parameter tuning. In the first experiment we clustered all examples, in the second experiment we clustered examples that remain after outlier removal. When removing outliers we obtained better clustering results in terms of Davies-Bouldin index.
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References
Breunig, M.M., Kreigel, H.P., Ng, R.T., Sander, J.: LOF: Identifying density-based local outliers. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD, pp. 93–104 (2000)
Choi, Y., et al.: Psychometric properties of the coping inventory for stressful situations in Korean adults. Psychiatry Invest. 14(4), 427–433 (2017)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-1(2), 224–227 (1979)
Endler, N.S., Parker, J.D.: Multidimensional assessment of coping: a critical evaluation. J. Pers. Soc. Psychol. 58(5), 844–854 (1990)
Janowski, K., Kurpas, D., Kusz, J., Mroczek, B., Jedynak, T.: Emotional control, styles of coping with stress and acceptance of illness among patients suffering from chronic somatic diseases. Stress. Health 30(1), 34–42 (2014)
Klein, D.M., Turvey, C.L., Pies, C.J.: Relationship of coping styles with quality of life and depressive symptoms in older heart failure patients. J. Aging Health 19(1), 22–38 (2007)
Bittles, A.H., Parsons, P.A. (eds.): Stress. Studies in Biology, Economy and Society, Palgrave Macmillan UK, London (1996). https://doi.org/10.1007/978-1-349-14163-0
Xu, H.Y., Yu, Y.J., Zhang, Q.H., Hu, H.Y., Li, M.: Tailored interventions to improve medication adherence for cardiovascular diseases. Front. Pharmacol. 11, 510339 (2020)
Acknowledgment
The initial study was financially supported by Wroclaw Medical University (activity of Laboratory for Applied Research on Cardiovascular System, ST–722). The current analyses were performed within the consortium HeartBIT 4.0 – Application of innovative Medical Data Science technologies for heart diseases funded from the European Union’s Horizon 2020 program, grant agreement number 857446.
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Berka, P., Pondel, M., Chudán, D., Siennicka, A. (2022). Towards Tailored Intervention in Medicine Using Patients’ Segmentation. In: Ceci, M., Flesca, S., Masciari, E., Manco, G., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2022. Lecture Notes in Computer Science(), vol 13515. Springer, Cham. https://doi.org/10.1007/978-3-031-16564-1_40
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DOI: https://doi.org/10.1007/978-3-031-16564-1_40
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