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
Open accessAbstract
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 (N= 973) using expert SLP manual phenotype coding as a gold-standard comparison and then applied and further validated in a replication sample of N= 13,652 EHRs. Results: In the discovery sample, the APT-DLD algorithm correctly classified 98% (concordance) of DLD cases in concordance with manually coded records in the training set, indicating that APT-DLD successfully mimics a comprehensive chart review. The output of APT-DLD was also validated in relation to independently conducted SLP clinician coding in a subset of records, with a positive predictive value of 95% of cases correctly classified as DLD. We also applied APT-DLD to the replication sample, where it achieved a positive predictive value of 90% in relation to SLP clinician classification of DLD. Conclusions: APT-DLD is a reliable, valid, and scalable tool for identifying DLD cohorts in EHRs. This new method has promising public health implications for future large-scale epidemiological investigations of DLD and may inform EHR data mining algorithms for other communication disorders. Supplemental Material: https://doi.org/10.23641/asha.12753578
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The majority of DLD cases in children are thought to remain unidentified.
“Additionally, the majority of DLD cases in children are thought to remain unidentified, further indicating a great need to improve the early detection process by establishing risk factors [10,15–17].”
From Tracking Acquisition of Language in Kids (TALK) study protocol: A longitudinal investigation of infants at high vs. low risk for atypical speech and language development (2026) · cites this paper as [17]
Experimental — accuracy not yet measured
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Millager, R. A., Feldman, J. I., Williams, Z. J., Shibata, K., Martinez-Torres, K. A., Bryan, K. M., Pruett, D. G., Mitchell, J., Markfeld, J. E., Merritt, B., et al. (2024) · Perspectives of the ASHA Special Interest Groups · doi:10.1044/2024_persp-23-00204 · Cited by 17 · see citing works
Edgcomb, J. B., Tseng, C., Pan, M., Klomhaus, A. M., & Zima, B. T. (2023) · JMIR Mental Health · doi:10.2196/47084 · Cited by 24 · see citing works
Nayak, S., Gustavson, D. E., Wang, Y., Below, J. E., Gordon, R. L., & Magne, C. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.765945 · Cited by 10 · see citing works
Self-citationChaparro-Moreno, L. J., Villasanti, H. G., Justice, L. M., Sun, J., & Schmitt, M. B. (2024) · Journal of Speech Language and Hearing Research · doi:10.1044/2024_jslhr-23-00310 · Cited by 6 · see citing works
Hu, Y. M., Ngai, C. S. B., & Chen, S. (2025) · Journal of Speech Language and Hearing Research · doi:10.1044/2025_jslhr-24-00488 · Cited by 4 · see citing works
Examining Early Vocabulary Growth Trajectories in Late Talkers in a Low‐Income Longitudinal Sample
Singletary, B., Jiang, H., Wilberforce, W. G., Avelar, D., Strother‐Garcia, K., & Justice, L. M. (2025) · Infancy · doi:10.1111/infa.70036 · Cited by 3 · see citing works
Millager, R. A., Feldman, J. I., Williams, Z. J., Shibata, K., Martinez-Torres, K. A., Bryan, K. M., Pruett, D. G., Mitchell, J., Markfeld, J. E., Merritt, B., et al. (2023) · doi:10.31234/osf.io/6hdmz · Cited by 4 · see citing works
Nayak, S., Ladányi, E., Eising, E., Mekki, Y., Nitin, R., Bush, C. T., Gustavson, D., Anglada-Tort, M., Lancaster, H., Mosing, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60867-2 · Cited by 7 · see citing works
Self-citationSingletary, B., Justice, L. M., Jiang, H., Wilberforce, W. G., & Avelar, D. (2025) · Early Childhood Research Quarterly · doi:10.1016/j.ecresq.2025.07.003 · Cited by 5 · see citing works
Nayak, S., Ladányi, E., Eising, E., Mekki, Y., Nitin, R., Bush, C. T., Gustavson, D. E., Anglada-Tort, M., Lancaster, H. S., Mosing, M. A., et al. (2024) · doi:10.31234/osf.io/kcgp5 · Cited by 2 · see citing works
Self-citationToki, E. I., Papadopoulou, S. L., & Pange, J. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-56075-0_19 · Cited by 1 · see citing works
Identifying primary-care features associated with complex mental health difficulties
McInerney, C. D., Oliver, P., Achinanya, A., Horspool, M. J., Huddy, V. C., & Burton, C. D. (2025) · PLoS ONE · doi:10.1371/journal.pone.0322771 · Cited by 1 · see citing works
Goble, G., Hennessy, V., Cheng, T.-H. Z., Rodda, A., Taulu, S., Gordon, R. L., & Zhao, T. C. (2026) · PLOS One · doi:10.1371/journal.pone.0335596
“Additionally, the majority of DLD cases in children are thought to remain unidentified, further indicating a great need to improve the early detection process by establishing risk factors [10,15–17].”
Self-citationJustice, L. M., Pelfrey, G. L., Jiang, H., Foster, T. J., Irvin, D., & Villasanti, H. G. (2026) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-026-07436-8
- Authors
- Walters, C. E., Nitin, R., Margulis, K., Boorom, O., Gustavson, D. E., Bush, C. T., Davis, L. K., Below, J. E., Cox, N. J., Camarata, S. M., & Gordon, R. L.
- Affiliations
- Vanderbilt University (US)
- Kennedy Krieger Institute (US)
- Vanderbilt University Medical Center (US)
- Corresponding author
- Gordon, Reyna L.
- ORCID
- Venue
- Journal of Speech, Language, and Hearing Research
- Published
- 2020-09-15
- Volume
- 63
- Issue
- 9
- Pages
- 3019-3035
- ISSN
- 1092-4388 (ISSN-L)
- 1558-9102
- PMC
- PMC7890229
- OpenAlex
- W3048790884
- Cited by
- 18
- References
- 89
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 4 · 2023: 3 · 2022: 3 · 2021: 1
- FWCI
- 2.64
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Speech Language and Hearing Research (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (UL1TR000445)
- National Institute on Deafness and Other Communication Disorders (R03DC014802, R01DC016977, R03DC015329, K18DC017383, R01DC017175)
- National Center for Advancing Translational Sciences
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (P50HD103537)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Keywords
- specific language impairment, automated phenotyping, electronic health records, ICD codes, communication disorders
- MeSH terms
- Algorithms, Humans, Language Development Disorders, Dimaprit, Data Mining, Electronic Health Records
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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