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We aim to create a framework for transfer learning using latent factor modelsto learn the dependence structure between a larger source dataset and a targetdataset. The methodology is motivated by our goal of building a risk-assessmentmodel for surgery patients, using both institutional and national surgicaloutcomes data. The national surgical outcomes data is collected through NSQIP(National Surgery Quality Improvement Program), a database housing almost 4million patients from over 700 different hospitals. We build a latent factormodel with a hierarchical prior on the loadings matrix to appropriately accountfor the different covariance structure in our data. We extend this model tohandle more complex relationships between the populations by deriving a scalemixture formulation using stick-breaking properties. Our model provides atransfer learning framework that utilizes all information from both the sourceand target data, while modeling the underlying inherent differences betweenthem.

Transfer Learning via Latent Factor Modeling to Improve Prediction of Surgical Complications