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

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

Abstract Background Rare diseases affect approximately 400 million people worldwide. Many of them suffer from delayed diagnosis. Among them, NPHP1-related renal ciliopathies need to be diagnosed as early as possible as potential treatments have been recently investigated with promising results. Our...

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Bibliographic Details
Main Authors: Carole Faviez, Marc Vincent, Nicolas Garcelon, Olivia Boyer, Bertrand Knebelmann, Laurence Heidet, Sophie Saunier, Xiaoyi Chen, Anita Burgun
Format: Article
Language:English
Published: BMC 2024-02-01
Series:Orphanet Journal of Rare Diseases
Subjects:
Diagnosis support
Electronic health record
Supervised machine learning
Semantic similarity
Imbalanced dataset
Rare disease
Online Access:https://doi.org/10.1186/s13023-024-03063-7

Internet

https://doi.org/10.1186/s13023-024-03063-7

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