A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions.

ABSTRACT: Deep learning algorithms trained on instances that violate the assumption of being independent and identically distributed (i.i.d.) are known to experience destructive interference, a phenomenon characterized by a degradation in performance. Such a violation, however, is ubiquitous in clinical settings where data are streamed temporally from different clinical sites and from a multitude of physiological sensors. To mitigate this interference, we propose a continual learning strategy, entitled CLOPS, that employs a replay buffer. To guide the storage of instances into the buffer, we propose end-to-end trainable parameters, termed task-instance parameters, that quantify the difficulty with which data points are classified by a deep-learning system. We validate the interpretation of these par

SUBMITTER: Kiyasseh D 

PROVIDER: S-EPMC8270996 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature