WellFactor: Patient Profiling using Integrative Embedding of Healthcare Data
This addresses the challenge of patient profiling in healthcare for platforms with varied data sources, though it appears incremental as it builds on existing embedding techniques.
The paper tackles the problem of integrating diverse healthcare data to create patient profiles, introducing WellFactor, which uses constrained low-rank approximation and task-specific labels to handle sparsity and improve embeddings, resulting in better classification performance, meaningful clustering, and consistent similarity searches compared to existing methods.
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.