Deep representation learning of electronic health records to unlock patient stratification at scale
Abstract Deriving disease subtypes from electronic health records (EHRs) can guide next-generation personalized medicine. However, challenges in summarizing and representing patient data prevent widespread practice of scalable EHR-based stratification analysis. Here we present an unsupervised framew...
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| Main Authors: | , , , , , , , , |
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| Format: | article |
| Language: | EN |
| Published: |
Nature Portfolio
2020
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| Subjects: | |
| Online Access: | https://doaj.org/article/a3c812cf99b140a5b237ea6ab049acf1 |
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