Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Feb 10;19(1):55.
doi: 10.1186/s13023-024-03063-7.

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

Carole Faviez  1   2 , Marc Vincent  3 , Nicolas Garcelon  4   5   3 , Olivia Boyer  6   7 , Bertrand Knebelmann  8 , Laurence Heidet  6 , Sophie Saunier  7 , Xiaoyi Chen  4   5   3 , Anita Burgun  4   5   9
Affiliations

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

Carole Faviez et al. Orphanet J Rare Dis. .

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.

Keywords: Diagnosis support; Electronic health record; Imbalanced dataset; Rare disease; Semantic similarity; Supervised machine learning.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Flowchart of the patient selection process
Fig. 2
Fig. 2
Flowchart of the evaluation process
Fig. 3
Fig. 3
UMAP with restricted hierarchical similarity. TP true positives; FN false negatives, i.e., cases always misclassified by the method, corresponding to patients P1–5
See this image and copyright information in PMC

References

    1. Faviez C, Chen X, Garcelon N, et al. Diagnosis support systems for rare diseases: a scoping review. Orphanet J Rare Dis. 2020;15(1):94. doi: 10.1186/s13023-020-01374-z. - DOI - PMC - PubMed
    1. Garcelon N, Burgun A, Salomon R, Neuraz A. Electronic health records for the diagnosis of rare diseases. Kidney Int. 2020;97(4):676–686. doi: 10.1016/j.kint.2019.11.037. - DOI - PubMed
    1. Hens D, Wyers L, Claeys KG. Validation of an Artificial Intelligence driven framework to automatically detect red flag symptoms in screening for rare diseases in electronic health records: hereditary transthyretin amyloidosis polyneuropathy as a key example. J Peripher Nervous Syst. 2023 doi: 10.1111/jns.12523. - DOI - PubMed
    1. Schaaf J, Sedlmayr M, Sedlmayr B, Storf H. User-centred development of a diagnosis support system for rare diseases. dHealth. 2022;2022:11–18. doi: 10.3233/SHTI220341. - DOI - PubMed
    1. Neuraz A, Lerner I, Digan W, et al. Natural language processing for rapid response to emergent diseases: case study of calcium channel blockers and hypertension in the COVID-19 pandemic. J Med Internet Res. 2020;22(8):e20773. doi: 10.2196/20773. - DOI - PMC - PubMed