Exploring long-term breast cancer survivors' care trajectories using dynamic time warping-based unsupervised clustering.

Autor:	Giannoula A; Epidemiology and Evaluation Department, Hospital del Mar Research Institute (IMIM), Barcelona, 08003, Spain.; Research Programme on Biomedical Informatics (GRIB), Department of Medicine and Life Sciences (MELIS), Hospital del Mar Research Institute (IMIM), Universitat Pompeu Fabra, Barcelona, Spain.; RICAPPS Red de Investigación en Cronicidad, Atención Primaria Y Promoción de la Salud, Spain., Comas M; Epidemiology and Evaluation Department, Hospital del Mar Research Institute (IMIM), Barcelona, 08003, Spain.; RICAPPS Red de Investigación en Cronicidad, Atención Primaria Y Promoción de la Salud, Spain., Castells X; Epidemiology and Evaluation Department, Hospital del Mar Research Institute (IMIM), Barcelona, 08003, Spain.; RICAPPS Red de Investigación en Cronicidad, Atención Primaria Y Promoción de la Salud, Spain., Estupiñán-Romero F; RICAPPS Red de Investigación en Cronicidad, Atención Primaria Y Promoción de la Salud, Spain.; Data Science for Health Services and Policy Research Group, Institute for Health Sciences (IACS), Zaragoza, Aragon, 50009, Spain., Bernal-Delgado E; RICAPPS Red de Investigación en Cronicidad, Atención Primaria Y Promoción de la Salud, Spain.; Data Science for Health Services and Policy Research Group, Institute for Health Sciences (IACS), Zaragoza, Aragon, 50009, Spain., Sanz F; Epidemiology and Evaluation Department, Hospital del Mar Research Institute (IMIM), Barcelona, 08003, Spain.; Research Programme on Biomedical Informatics (GRIB), Department of Medicine and Life Sciences (MELIS), Hospital del Mar Research Institute (IMIM), Universitat Pompeu Fabra, Barcelona, Spain., Sala M; Epidemiology and Evaluation Department, Hospital del Mar Research Institute (IMIM), Barcelona, 08003, Spain.; RICAPPS Red de Investigación en Cronicidad, Atención Primaria Y Promoción de la Salud, Spain.
Jazyk:	angličtina
Zdroj:	Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2024 Apr 03; Vol. 31 (4), pp. 820-831.
DOI:	10.1093/jamia/ocad251
Abstrakt:	Objectives: Long-term breast cancer survivors (BCS) constitute a complex group of patients, whose number is estimated to continue rising, such that, a dedicated long-term clinical follow-up is necessary. Materials and Methods: A dynamic time warping-based unsupervised clustering methodology is presented in this article for the identification of temporal patterns in the care trajectories of 6214 female BCS of a large longitudinal retrospective cohort of Spain. The extracted care-transition patterns are graphically represented using directed network diagrams with aggregated patient and time information. A control group consisting of 12 412 females without breast cancer is also used for comparison. Results: The use of radiology and hospital admission are explored as patterns of special interest. In the generated networks, a more intense and complex use of certain healthcare services (eg, radiology, outpatient care, hospital admission) is shown and quantified for the BCS. Higher mortality rates and numbers of comorbidities are observed in various transitions and compared with non-breast cancer. It is also demonstrated how a wealth of patient and time information can be revealed from individual service transitions. Discussion: The presented methodology permits the identification and descriptive visualization of temporal patterns of the usage of healthcare services by the BCS, that otherwise would remain hidden in the trajectories. Conclusion: The results could provide the basis for better understanding the BCS' circulation through the health system, with a view to more efficiently predicting their forthcoming needs and thus designing more effective personalized survivorship care plans. (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)
Databáze:	MEDLINE
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