Abstract
In this paper, we presented the results of data analysis of 1-year measurements from diabetes patients within the Slovenian healthcare project eCare. We focused on looking for groups/clusters of patients with the similar time profile of the glucose values and describe those patients with their clinical status. We treated in a similar way the WONCA scores (i.e., patients’ functional status). Considering the complexity of the data at hand (time series with a different number of measurements and different time intervals), we used predictive clustering trees with dynamic time warping as the distance between time series. The obtained PCTs identified several groups of patients that exhibit similar behavior. More specifically, we described groups of patients that are able to keep under control their disease, and groups that are less successful in that. Furthermore, we identified and described groups of patients that have similar functional status.
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Beštek, M., Kocev, D., Džeroski, S., Brodnik, A., Iljaž, R. (2017). Modelling Time-Series of Glucose Measurements from Diabetes Patients Using Predictive Clustering Trees. In: ten Teije, A., Popow, C., Holmes, J., Sacchi, L. (eds) Artificial Intelligence in Medicine. AIME 2017. Lecture Notes in Computer Science(), vol 10259. Springer, Cham. https://doi.org/10.1007/978-3-319-59758-4_11
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