Performance and clinical utility of a new supervised machine-learning pipeline in detecting rare ciliopathy patients based on deep phenotyping from electronic health records and semantic similarity.

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Title: Performance and clinical utility of a new supervised machine-learning pipeline in detecting rare ciliopathy patients based on deep phenotyping from electronic health records and semantic similarity.
Authors: Faviez, Carole1,2 (AUTHOR) carole.faviez@inserm.fr, Vincent, Marc3 (AUTHOR), Garcelon, Nicolas1,2,3 (AUTHOR), Boyer, Olivia4,5 (AUTHOR), Knebelmann, Bertrand6 (AUTHOR), Heidet, Laurence4 (AUTHOR), Saunier, Sophie5 (AUTHOR), Chen, Xiaoyi1,2,3 (AUTHOR), Burgun, Anita1,2,7 (AUTHOR)
Source: Orphanet Journal of Rare Diseases. 2/10/2024, p1-12. 12p.
Database: Academic Search Ultimate
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  Data: Performance and clinical utility of a new supervised machine-learning pipeline in detecting rare ciliopathy patients based on deep phenotyping from electronic health records and semantic similarity.
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  Data: <searchLink fieldCode="JN" term="%22Orphanet+Journal+of+Rare+Diseases%22">Orphanet Journal of Rare Diseases</searchLink>. 2/10/2024, p1-12. 12p.
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        Value: 10.1186/s13023-024-03063-7
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      – TitleFull: Performance and clinical utility of a new supervised machine-learning pipeline in detecting rare ciliopathy patients based on deep phenotyping from electronic health records and semantic similarity.
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              Text: 2/10/2024
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