FLKit is a new toolkit with four lifecycle stages, eleven role-specific entry points, a glossary, FL Story template, and tool directory to support federated learning projects in health and life sciences.
Feasibility and utility of applications of the common data model to multiple, disparate observational health databases
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2representative citing papers
A fine-tuned deep learning model using systemic EHR data achieved AUROC 0.883 and PPV 0.657 for identifying glaucoma in a held-out Stanford cohort of over 20,000 patients.
citing papers explorer
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Development and Design of FLKit: A Structured Onboarding Toolkit for Federated Learning in Health and Life Sciences
FLKit is a new toolkit with four lifecycle stages, eleven role-specific entry points, a glossary, FL Story template, and tool directory to support federated learning projects in health and life sciences.
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Validating a Deep Learning Algorithm to Identify Patients with Glaucoma using Systemic Electronic Health Records
A fine-tuned deep learning model using systemic EHR data achieved AUROC 0.883 and PPV 0.657 for identifying glaucoma in a held-out Stanford cohort of over 20,000 patients.