Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods. | D2R Server publishing the DBLP Bibliography Database, hosted at L3S Research Center

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dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Mirza_Farhan_Bin_Tarek>
dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Rahmatollah_Beheshti>
dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Raphael_Poulain>
foaf:homepage	<http://dx.doi.org/doi.org%2F10.1145%2F3593013.3594102>
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dc:identifier	DBLP conf/fat/PoulainTB23 (xsd:string)
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dcterms:issued	2023 (xsd:gYear)
rdfs:label	Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods. (xsd:string)
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foaf:maker	<https://dblp.l3s.de/d2r/resource/authors/Raphael_Poulain>
swrc:pages	1599-1608 (xsd:string)
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dc:title	Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods. (xsd:string)
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