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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...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1038/s41467-021-24483-0

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
St Cross College
Role:
Author
ORCID:
0000-0002-2898-1790
Department of Health More from this funder
Publisher:
Springer Nature Publisher's website
Journal:
Nature Communications Journal website
Volume:
12
Issue:
1
Article number:
4221
Publication date:
2021-07-09
Acceptance date:
2021-06-11
DOI:
EISSN:
2041-1723
Pmid:
34244504
Language:
English
Keywords:
Pubs id:
1186723
Local pid:
pubs:1186723
Deposit date:
2021-07-27

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