Patient Clustering via Integrated Profiling of Clinical and Digital Data
This addresses patient profiling for healthcare applications, but it is incremental as it builds on existing low-rank methods with new data integration.
The paper tackled patient clustering by integrating clinical and digital interaction data using constrained low-rank approximation, resulting in superior clustering coherence and recommendation accuracy compared to baselines.
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.