1
|
Faviez C, Vincent M, Garcelon N, Boyer O, Knebelmann B, Heidet L, Saunier S, Chen X, Burgun A. 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. Orphanet J Rare Dis 2024; 19:55. [PMID: 38336713 PMCID: PMC10858490 DOI: 10.1186/s13023-024-03063-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 02/03/2024] [Indexed: 02/12/2024] Open
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 objective was to develop a supervised machine learning pipeline for the detection of NPHP1 ciliopathy patients from a large number of nephrology patients using electronic health records (EHRs). METHODS AND RESULTS We designed a pipeline combining a phenotyping module re-using unstructured EHR data, a semantic similarity module to address the phenotype dependence, a feature selection step to deal with high dimensionality, an undersampling step to address the class imbalance, and a classification step with multiple train-test split for the small number of rare cases. The pipeline was applied to thirty NPHP1 patients and 7231 controls and achieved good performances (sensitivity 86% with specificity 90%). A qualitative review of the EHRs of 40 misclassified controls showed that 25% had phenotypes belonging to the ciliopathy spectrum, which demonstrates the ability of our system to detect patients with similar conditions. CONCLUSIONS Our pipeline reached very encouraging performance scores for pre-diagnosing ciliopathy patients. The identified patients could then undergo genetic testing. The same data-driven approach can be adapted to other rare diseases facing underdiagnosis challenges.
Collapse
Affiliation(s)
- Carole Faviez
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM UMR 1138, 75006, Paris, France.
- Inria, 75012, Paris, France.
| | - Marc Vincent
- Université Paris Cité, Imagine Institute, Data Science Platform, INSERM UMR 1163, 75015, Paris, France
| | - Nicolas Garcelon
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM UMR 1138, 75006, Paris, France
- Inria, 75012, Paris, France
- Université Paris Cité, Imagine Institute, Data Science Platform, INSERM UMR 1163, 75015, Paris, France
| | - Olivia Boyer
- Department of Pediatric Nephrology, APHP-Centre, Reference Center for Inherited Renal Diseases (MARHEA), Imagine Institute, Hôpital Necker-Enfants Malades, Université Paris Cité, 75015, Paris, France
- Laboratory of Renal Hereditary Diseases, INSERM UMR 1163, Imagine Institute, Université Paris Cité, 75015, Paris, France
| | - Bertrand Knebelmann
- Nephrology and Transplantation Department, MARHEA, Hôpital Necker-Enfants Malades, AP-HP, Université Paris Cité, 75015, Paris, France
| | - Laurence Heidet
- Department of Pediatric Nephrology, APHP-Centre, Reference Center for Inherited Renal Diseases (MARHEA), Imagine Institute, Hôpital Necker-Enfants Malades, Université Paris Cité, 75015, Paris, France
| | - Sophie Saunier
- Laboratory of Renal Hereditary Diseases, INSERM UMR 1163, Imagine Institute, Université Paris Cité, 75015, Paris, France
| | - Xiaoyi Chen
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM UMR 1138, 75006, Paris, France
- Inria, 75012, Paris, France
- Université Paris Cité, Imagine Institute, Data Science Platform, INSERM UMR 1163, 75015, Paris, France
| | - Anita Burgun
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM UMR 1138, 75006, Paris, France
- Inria, 75012, Paris, France
- Département d'informatique Médicale, Hôpital Necker-Enfants Malades, AP-HP, 75015, Paris, France
| |
Collapse
|