Methods for identifying homogeneous groups with varying characteristics in longitudinal data have been receiving increasing attention in recent years, especially in the medical domain. Exploiting electronic health records (EHRs) to infer patient subtypes can support practitioners in improving the decision-making process. In this paper, we propose a dynamic method for clustering multivariate longitudinal data, which constitutes a transparent solution for patient subtyping and modeling disease progression. Based on the assumption that subjects with similar disease trajectories share the same patterns, we subtype patients based on their medical history then learn the disease progression model. We cluster data periodically, and maintain the results and update the deduced subtypes by applying a borrowed approach from the data integration domain, namely entity matching. We test our method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) real-world dataset. We demonstrate how our results can facilitate the early detection of dementia two years on average before the actual diagnosis, and therefore assist in the development of support and prevention procedures for patients. We compare our clustering with the state-of-the-art data stream clustering algorithms, and show that our method exhibits higher effectiveness in terms of both internal and external validation metrics.