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

Abstract

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.

Keywords: breast cancer survivors; data mining; healthcare services; longitudinal analysis.