  In the rapidly evolving healthcare industry, platforms now have access to not
only traditional medical records, but also diverse data sets encompassing
various patient interactions, such as those from healthcare web portals. To
address this rich diversity of data, we introduce WellFactor: a method that
derives patient profiles by integrating information from these sources. Central
to our approach is the utilization of constrained low-rank approximation.
WellFactor is optimized to handle the sparsity that is often inherent in
healthcare data. Moreover, by incorporating task-specific label information,
our method refines the embedding results, offering a more informed perspective
on patients. One important feature of WellFactor is its ability to compute
embeddings for new, previously unobserved patient data instantaneously,
eliminating the need to revisit the entire data set or recomputing the
embedding. Comprehensive evaluations on real-world healthcare data demonstrate
WellFactor's effectiveness. It produces better results compared to other
existing methods in classification performance, yields meaningful clustering of
patients, and delivers consistent results in patient similarity searches and
predictions.
