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Discovering monogenic patients with a confirmed molecular diagnosis in millions of clinical notes with MonoMiner

  • David Wei Wu
    Affiliations
    Department of Computer Science, Stanford University School of Engineering, Stanford, CA

    Medical Scientist Training Program, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
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  • Jonathan A. Bernstein
    Affiliations
    Department of Pediatrics, Stanford University School of Medicine, Stanford, CA
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  • Gill Bejerano
    Correspondence
    Correspondence and requests for materials should be addressed to Gill Bejerano, Department of Computer Science, Stanford School of Engineering, Stanford University, Beckman Center B-300, 279 Campus Drive West (MC 5329), Stanford, CA 94305-5329
    Affiliations
    Department of Computer Science, Stanford University School of Engineering, Stanford, CA

    Department of Pediatrics, Stanford University School of Medicine, Stanford, CA

    Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA

    Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA
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Published:August 17, 2022DOI:https://doi.org/10.1016/j.gim.2022.07.008

      ABSTRACT

      Purpose

      Cohort building is a powerful foundation for improving clinical care, performing biomedical research, recruiting for clinical trials, and many other applications. We set out to build a cohort of all monogenic patients with a definitive causal gene diagnosis in a 3-million patient hospital system.

      Methods

      We define a subset (4461) of OMIM diseases that have at least 1 known monogenic causal gene. We then introduce MonoMiner, a natural language processing framework to identify molecularly confirmed monogenic patients from free-text clinical notes.

      Results

      We show that ICD-10-CM codes cover only a fraction of monogenic diseases and that even where available, ICD-10-CM code‒based patient retrieval offers 0.14 precision. Searching by causal gene symbol offers great recall but has an even worse 0.07 precision. MonoMiner achieves 6 to 11 times higher precision (0.80), with 0.87 precision on disease diagnosis alone, tagging 4259 patients with 560 monogenic diseases and 534 causal genes, at 0.48 recall.

      Conclusion

      MonoMiner enables the discovery of a large, high-precision cohort of patients with monogenic diseases with an established molecular diagnosis, empowering numerous downstream uses. Because it relies solely on clinical notes, MonoMiner is highly portable, and its approach is adaptable to other domains and languages.

      Keywords

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