ArticleJournal of fluency disorders2021
Identifying developmental stuttering and associated comorbidities in electronic health records and creating a phenome risk classifier.
Article in Journal of fluency disorders, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 38 citations in OpenAlex.
- Developmental stuttering with common and complex phenotypes.Developmental medicine and child neurology · 2026Article
- Article
- Challenges and Opportunities in Characterizing the Genetics of Stuttering: From Sample Acquisition to Functional Interpretation of the Genome.Journal of speech, language, and hearing research : JSLHR · 2025Review
- Unraveling Stuttering Through a Multi-Omics Lens.Life (Basel, Switzerland) · 2025Article
- The clinical and genetic spectrum of paediatric speech and language disorders.Brain : a journal of neurology · 2025Article
- Stuttering: Our Current Knowledge, Research Opportunities, and Ways to Address Critical Gaps.Neurobiology of language (Cambridge, Mass.) · 2025Review
- An active inference account of stuttering behavior.Frontiers in human neuroscience · 2025Article
- Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field.Journal of neurodevelopmental disorders · 2024Review
- The clinical and genetic spectrum of paediatric speech and language disorders in 52,143 individuals.medRxiv : the preprint server for health sciences · 2024Article
- Next-generation phenotyping: introducing phecodeX for enhanced discovery research in medical phenomics.Bioinformatics (Oxford, England) · 2023Article
- A Preliminary Study of Speech Rhythm Differences as Markers of Stuttering Persistence in Preschool-Age Children.Journal of speech, language, and hearing research : JSLHR · 2023Article
- Acoustic analysis in stuttering: a machine-learning study.Frontiers in neurology · 2023Article
- Association of Developmental Language Disorder With Comorbid Developmental Conditions Using Algorithmic Phenotyping.JAMA network open · 2022Article
- Computational analysis of neurodevelopmental phenotypes: Harmonization empowers clinical discovery.Human mutation · 2022Review
- Population-based genetic effects for developmental stuttering.HGG advances · 2022Article
- Nonword Repetition Performance Differentiates Children Who Stutter With and Without Concomitant Speech Sound and Developmental Language Disorders.Journal of speech, language, and hearing research : JSLHR · 2022Article
- The Musical Abilities, Pleiotropy, Language, and Environment (MAPLE) Framework for Understanding Musicality-Language Links Across the Lifespan.Neurobiology of language (Cambridge, Mass.) · 2022Review
- Test of Prosody via Syllable Emphasis ("TOPsy"): Psychometric Validation of a Brief Scalable Test of Lexical Stress Perception.Frontiers in neuroscience · 2022Article
- Phenome risk classification enables phenotypic imputation and gene discovery in developmental stuttering.American journal of human genetics · 2021Article
- Characterizing drug-induced stuttering in electronic health records.Journal of communication disordersArticle
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
purposeThis study aimed to identify cases of developmental stuttering and associated comorbidities in de-identified electronic health records (EHRs) at Vanderbilt University Medical Center, and, in turn, build and test a stuttering prediction model.
methodsA multi-step process including a keyword search of medical notes, a text-mining algorithm, and manual review was employed to identify stuttering cases in the EHR. Confirmed cases were compared to matched controls in a phenotype code (phecode) enrichment analysis to reveal conditions associated with stuttering (i.e., comorbidities). These associated phenotypes were used as proxy variables to phenotypically predict stuttering in subjects within the EHR that were not otherwise identifiable using the multi-step identification process described above.
resultsThe multi-step process resulted in the manually reviewed identification of 1,143 stuttering cases in the EHR. Highly enriched phecodes included codes related to childhood onset fluency disorder, adult-onset fluency disorder, hearing loss, sleep disorders, atopy, a multitude of codes for infections, neurological deficits, and body weight. These phecodes were used as variables to create a phenome risk classifier (PheRC) prediction model to identify additional high likelihood stuttering cases. The PheRC prediction model resulted in a positive predictive value of 83 %.
conclusionsThis study demonstrates the feasibility of using EHRs in the study of stuttering and found phenotypic associations. The creation of the PheRC has the potential to enable future studies of stuttering using existing EHR data, including investigations into the genetic etiology.
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