Privacy-preserving federated prediction of pain intensity change based on multi-center survey data
Privacy-preserving federated prediction of pain intensity change based on multi-center survey data
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Background: Patient-reported survey data are used to train prognostic models aimed at improving healthcare. However, such data are typically available multi-centric and, for privacy reasons, cannot easily be centralized in one data repository. Models trained locally are less accurate, robust, and generalizable. We present and apply privacy-preservi...
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Privacy-preserving federated prediction of pain intensity change based on multi-center survey data
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TN_cdi_proquest_journals_3104283581
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_3104283581
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2331-8422
