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De-black-boxing health AI: demonstrating reproducible machine learning computable phenotypes using the N3C-RECOVER Long COVID model in the All of Us data repository.

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Title: De-black-boxing health AI: demonstrating reproducible machine learning computable phenotypes using the N3C-RECOVER Long COVID model in the All of Us data repository.
Authors: Pfaff, Emily R1, epfaff@email.unc.edu, Girvin, Andrew T2, Crosskey, Miles3, Gangireddy, Srushti4, Master, Hiral5, Wei, Wei-Qi4, Kerchberger, V Eric6, Weiner, Mark7, Harris, Paul A4, Basford, Melissa5, Lunt, Chris8, Chute, Christopher G9, Moffitt, Richard A10, Haendel, Melissa11, Consortia, N3C and RECOVER
Source: Journal of the American Medical Informatics Association; Jul2023, Vol. 30 Issue 7, p1305-1312, 8p, 3 Charts, 2 Graphs
Database: Applied Science & Technology Source
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DbLabel: Applied Science & Technology Source
An: 164550118
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: De-black-boxing health AI: demonstrating reproducible machine learning computable phenotypes using the N3C-RECOVER Long COVID model in the All of Us data repository.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+the+American+Medical+Informatics+Association%22">Journal of the American Medical Informatics Association</searchLink>; Jul2023, Vol. 30 Issue 7, p1305-1312, 8p, 3 Charts, 2 Graphs
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=164550118
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        Value: 10.1093/jamia/ocad077
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      – Code: eng
        Text: English
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        PageCount: 8
        StartPage: 1305
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      – TitleFull: De-black-boxing health AI: demonstrating reproducible machine learning computable phenotypes using the N3C-RECOVER Long COVID model in the All of Us data repository.
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              Text: Jul2023
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              Y: 2023
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