4TaStiC: Time and trend traveling time series clustering for classifying long-term type 2 diabetes patients
This addresses the problem of efficient patient classification for doctors managing type 2 diabetes, though it appears incremental as it builds on existing dissimilarity measures.
The paper tackles the challenge of clustering diabetes patients with irregularly sampled time series data by introducing the 4TaStiC algorithm, which combines Euclidean and Pearson correlation metrics to capture both level differences and trends. Results show it outperforms seven existing methods on artificial datasets and successfully clusters 1,989 type 2 diabetes patients into groups with clear clinical characteristics.
Diabetes is one of the most prevalent diseases worldwide, characterized by persistently high blood sugar levels, capable of damaging various internal organs and systems. Diabetes patients require routine check-ups, resulting in a time series of laboratory records, such as hemoglobin A1c, which reflects each patient's health behavior over time and informs their doctor's recommendations. Clustering patients into groups based on their entire time series data assists doctors in making recommendations and choosing treatments without the need to review all records. However, time series clustering of this type of dataset introduces some challenges; patients visit their doctors at different time points, making it difficult to capture and match trends, peaks, and patterns. Additionally, two aspects must be considered: differences in the levels of laboratory results and differences in trends and patterns. To address these challenges, we introduce a new clustering algorithm called Time and Trend Traveling Time Series Clustering (4TaStiC), using a base dissimilarity measure combined with Euclidean and Pearson correlation metrics. We evaluated this algorithm on artificial datasets, comparing its performance with that of seven existing methods. The results show that 4TaStiC outperformed the other methods on the targeted datasets. Finally, we applied 4TaStiC to cluster a cohort of 1,989 type 2 diabetes patients at Siriraj Hospital. Each group of patients exhibits clear characteristics that will benefit doctors in making efficient clinical decisions. Furthermore, the proposed algorithm can be applied to contexts outside the medical field.