Research graph
References from Privacy-preserving federated machine learning on FAIR health data: A real-world application. Local targets link to admitted publications; unresolved targets remain external evidence.
10.1016/s0140-6736(21)00203-8
10.1016/s0140-6736(21)00203-8 · External reference
Digital health and the challenge of health systems transformation
10.21037/mhealth.2017.07.02 · 2017 · External reference
10.1016/j.jbi.2018.03.014
10.1016/j.jbi.2018.03.014 · External reference
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Applications of machine learning in real-life digital health interventions: review of the literature
10.2196/12286 · 2019 · External reference
Machine learning for health informatics
2016 · External reference
Federated machine learning
2019 · External reference
Privacy preserving distributed machine learning with federated learning
10.1016/j.comcom.2021.02.014 · 2021 · External reference
10.1561/2200000083 · 2021 · Admitted local publication
FeARH: Federated machine learning with anonymous random hybridization on electronic medical records
10.1016/j.jbi.2021.103735 · 2021 · External reference
Distributed analytics on sensitive medical data: the personal health train
10.1162/dint_a_00032 · 2020 · External reference
Data preparation for machine learning: data cleaning, feature selection, and data transforms in Python
2020 · External reference
10.1038/sdata.2016.18
10.1038/sdata.2016.18 · External reference
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Towards the tipping point for FAIR implementation
10.1162/dint_a_00049 · 2020 · External reference
From raw data to FAIR data: the FAIRification workflow for health research
10.1055/s-0040-1713684 · 2020 · External reference
Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic
10.1186/s13326-022-00263-7 · 2022 · External reference
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A federated semantic metadata registry framework for enabling interoperability across clinical research and care domains
10.1016/j.jbi.2013.05.009 · 2013 · External reference
Federated learning of deep networks using model averaging
2016 · External reference
A data transformation methodology to create findable, accessible, interoperable, and reusable health data: software design, development, and evaluation study
10.2196/42822 · 2023 · External reference
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Exploiting the cost (in) sensitivity of decision tree splitting criteria
2000 · External reference
Predicting 30-day readmission risk for patients with chronic obstructive pulmonary disease through a federated machine learning architecture on findable, accessible, interoperable, and reusable (FAIR) data: development and validation study
10.2196/35307 · 2022 · External reference
Applying the FAIR4Health solution to identify multimorbidity patterns and their association with mortality through a frequent pattern growth association algorithm
10.3390/ijerph19042040 · 2022 · External reference
Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review
10.1136/bmj.n2281 · 2021 · External reference
10.1093/ije/dyu188
10.1093/ije/dyu188 · External reference
Federated learning of electronic health records to improve mortality prediction in hospitalized patients with COVID-19: machine learning approach
10.2196/24207 · 2021 · External reference
Privacy-first health research with federated learning
2021 · External reference
FAIR4Health: findable, accessible, interoperable and reusable data to foster health research
2022 · External reference
