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  1. Article ; Online: Natural language processing of clinical notes enables early inborn error of immunity risk ascertainment.

    Roberts, Kirk / Chin, Aaron T / Loewy, Klaus / Pompeii, Lisa / Shin, Harold / Rider, Nicholas L

    The journal of allergy and clinical immunology. Global

    2024  Volume 3, Issue 2, Page(s) 100224

    Abstract: Background: There are now approximately 450 discrete inborn errors of immunity (IEI) described; however, diagnostic rates remain suboptimal. Use of structured health record data has proven useful for patient detection but may be augmented by natural ... ...

    Abstract Background: There are now approximately 450 discrete inborn errors of immunity (IEI) described; however, diagnostic rates remain suboptimal. Use of structured health record data has proven useful for patient detection but may be augmented by natural language processing (NLP). Here we present a machine learning model that can distinguish patients from controls significantly in advance of ultimate diagnosis date.
    Objective: We sought to create an NLP machine learning algorithm that could identify IEI patients early during the disease course and shorten the diagnostic odyssey.
    Methods: Our approach involved extracting a large corpus of IEI patient clinical-note text from a major referral center's electronic health record (EHR) system and a matched control corpus for comparison. We built text classifiers with simple machine learning methods and trained them on progressively longer time epochs before date of diagnosis.
    Results: The top performing NLP algorithm effectively distinguished cases from controls robustly 36 months before ultimate clinical diagnosis (area under precision recall curve > 0.95). Corpus analysis demonstrated that statistically enriched, IEI-relevant terms were evident 24+ months before diagnosis, validating that clinical notes can provide a signal for early prediction of IEI.
    Conclusion: Mining EHR notes with NLP holds promise for improving early IEI patient detection.
    Language English
    Publishing date 2024-02-02
    Publishing country United States
    Document type Journal Article
    ISSN 2772-8293
    ISSN (online) 2772-8293
    DOI 10.1016/j.jacig.2024.100224
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Validating inborn error of immunity prevalence and risk with nationally representative electronic health record data.

    Rider, Nicholas L / Truxton, Ahuva / Ohrt, Tracy / Margolin-Katz, Irene / Horan, Mary / Shin, Harold / Davila, Roger / Tenembaum, Vanessa / Quinn, Jessica / Modell, Vicki / Modell, Fred / Orange, Jordan S / Branner, Almut / Senerchia, Cynthia

    The Journal of allergy and clinical immunology

    2024  

    Abstract: Background: The 10 Warning Signs of Primary Immunodeficiency were created 30 years ago to advance recognition of inborn errors of immunity (IEI). However, no population-level assessment of their utility applied to electronic health record (EHR) data has ...

    Abstract Background: The 10 Warning Signs of Primary Immunodeficiency were created 30 years ago to advance recognition of inborn errors of immunity (IEI). However, no population-level assessment of their utility applied to electronic health record (EHR) data has been conducted.
    Objective: We sought to quantify the value of having ≥2 warning signs (WS) toward diagnosing IEI using a highly representative real-world US cohort. A secondary goal was estimating the US prevalence of IEI.
    Methods: In this cohort study, we accessed normalized and de-identified EHR data on 152 million US patients. An IEI cohort (n = 41,080), in which patients were defined by having at least 1 verifiable IEI diagnosis placed ≥2 times in their record, was compared with a matched set of controls (n = 250,262). WS were encoded along with relevant diagnoses, relative weights were calculated, and the proportion of IEI cases versus controls with ≥2 WS was compared.
    Results: The proportion of IEI cases with ≥2 WS significantly differed from controls (0.33 vs 0.031; P < .0005, χ
    Conclusions: This nationally representative US-based cohort study demonstrates that presence of WS and associated clinical diagnoses can facilitate identification of patients with IEI from EHR data. In addition, we estimate that 6 in 10,000, or approximately 150,000 to 200,000 individuals are affected by IEI across the United States.
    Language English
    Publishing date 2024-01-24
    Publishing country United States
    Document type Journal Article
    ZDB-ID 121011-7
    ISSN 1097-6825 ; 1085-8725 ; 0091-6749
    ISSN (online) 1097-6825 ; 1085-8725
    ISSN 0091-6749
    DOI 10.1016/j.jaci.2024.01.011
    Database MEDical Literature Analysis and Retrieval System OnLINE

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