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A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions
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 setti...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8270996/ https://www.ncbi.nlm.nih.gov/pubmed/34244504 http://dx.doi.org/10.1038/s41467-021-24483-0 |