Transfer-learning on federated observational healthcare data for prediction models using Bayesian sparse logistic regression with informed priors

Abstract

Transfer-learning using informed priors can help fine-tune prediction models in small datasets suffering from a lack of information. One large benefit is in that the prior is not dependent on patient-level information, such that we can conduct transfer-learning without violating privacy. In future work, the model can be applied for learning between disparate databases, or similar lack-of-information cases such as rare outcome prediction.

Ähnliche Arbeiten