Autoencoder Framework

Fillmore and his collaborators developed a novel autoencoder framework for hypersphere clustering to analyze high-dimensional, sparse count vectors of clinical actions. This approach enables the more accurate characterization of healthcare providers based on their specific patterns of prescribing medications and performing procedures. The framework demonstrated superior performance compared to traditional two-step projected K-means and K-medoids methods for identifying provider behavior within Medicare claims data.