Leveraging clinical data across healthcare institutions for continual learning of predictive risk models.

ABSTRACT: The inherent flexibility of machine learning-based clinical predictive models to learn from episodes of patient care at a new institution (site-specific training) comes at the cost of performance degradation when applied to external patient cohorts. To exploit the full potential of cross-institutional clinical big data, machine learning systems must gain the ability to transfer their knowledge across institutional boundaries and learn from new episodes of patient care without forgetting previously learned patterns. In this work, we developed a privacy-preserving learning algorithm named WUPERR (Weight Uncertainty Propagation and Episodic Representation Replay) and validated the algorithm in the context of early prediction of sepsis using data from over 104,000 patients across four distinct

SUBMITTER: Amrollahi F 

PROVIDER: S-EPMC9117839 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature