ArticleJournal of speech, language, and hearing research : JSLHR2020
Automated Phenotyping Tool for Identifying Developmental Language Disorder Cases in Health Systems Data (APT-DLD): A New Research Algorithm for Deployment in Large-Scale Electronic Health Record Systems.
Article in Journal of speech, language, and hearing research : JSLHR, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Musical rhythm abilities and risk for developmental speech-language problems and disorders: epidemiological and polygenic associations.Nature communications · 2025Pooled it
- Article
- Identifying primary-care features associated with complex mental health difficulties.PloS one · 2025Article
- Diversity of Research Participant Gender, Race, and Ethnicity in Communication Sciences and Disorders: A Systematic Review and Quantitative Synthesis of American Speech-Language-Hearing Association Publications in 2020.Perspectives of the ASHA special interest groups · 2024Article
- Assessing Detection of Children With Suicide-Related Emergencies: Evaluation and Development of Computable Phenotyping Approaches.JMIR mental health · 2023Article
- Association of Developmental Language Disorder With Comorbid Developmental Conditions Using Algorithmic Phenotyping.JAMA network open · 2022Article
- Test of Prosody via Syllable Emphasis ("TOPsy"): Psychometric Validation of a Brief Scalable Test of Lexical Stress Perception.Frontiers in neuroscience · 2022Article
- The Musical Abilities, Pleiotropy, Language, and Environment (MAPLE) Framework for Understanding Musicality-Language Links Across the Lifespan.Neurobiology of language (Cambridge, Mass.) · 2022Review
- Identifying developmental stuttering and associated comorbidities in electronic health records and creating a phenome risk classifier.Journal of fluency disorders · 2021Article
- Examining Early Vocabulary Growth Trajectories in Late Talkers in a Low-Income Longitudinal Sample.Infancy : the official journal of the International Society on Infant StudiesArticle
- Modeling the mechanisms through which conditions of poverty are associated with late language emergence in young children.Early childhood research quarterlyArticle
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Purpose Data mining algorithms using electronic health records (EHRs) are useful in large-scale population-wide studies to classify etiology and comorbidities (Casey et al., 2016). Here, we apply this approach to developmental language disorder (DLD), a prevalent communication disorder whose risk factors and epidemiology remain largely undiscovered. Method We first created a reliable system for manually identifying DLD in EHRs based on speech-language pathologist (SLP) diagnostic expertise. We then developed and validated an automated algorithmic procedure, called, Automated Phenotyping Tool for identifying DLD cases in health systems data (APT-DLD), that classifies a DLD status for patients within EHRs on the basis of ICD (International Statistical Classification of Diseases and Related Health Problems) codes. APT-DLD was validated in a discovery sample (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.