Deep representation learning of electronic health records to unlock patient stratification at scale
Deep representation learning of electronic health records to unlock patient stratification at scale
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Publisher
London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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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 framework based on deep learning to process heterogeneous EHRs an...
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Deep representation learning of electronic health records to unlock patient stratification at scale
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TN_cdi_doaj_primary_oai_doaj_org_article_a3c812cf99b140a5b237ea6ab049acf1
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_a3c812cf99b140a5b237ea6ab049acf1
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ISSN
2398-6352
E-ISSN
2398-6352
DOI
10.1038/s41746-020-0301-z
