  We introduce a novel profile-based patient clustering model designed for
clinical data in healthcare. By utilizing a method grounded on constrained
low-rank approximation, our model takes advantage of patients' clinical data
and digital interaction data, including browsing and search, to construct
patient profiles. As a result of the method, nonnegative embedding vectors are
generated, serving as a low-dimensional representation of the patients. Our
model was assessed using real-world patient data from a healthcare web portal,
with a comprehensive evaluation approach which considered clustering and
recommendation capabilities. In comparison to other baselines, our approach
demonstrated superior performance in terms of clustering coherence and
recommendation accuracy.
