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Related Experiment Video

Updated: Jun 16, 2026

Portable Intermodal Preferential Looking (IPL): Investigating Language Comprehension in Typically Developing Toddlers and Young Children with Autism

Portable Intermodal Preferential Looking (IPL): Investigating Language Comprehension in Typically Developing Toddlers and Young Children with Autism

Published on: December 14, 2012

Using clinical notes to identify children with speech-language delay and understand differences in diagnostic timing.

Jiang Shu¹, Danai Kasambira Fannin^2,3, Geraldine Dawson⁴

¹Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27705, United States.

|June 15, 2026

Summary

This summary is machine-generated.

Pediatricians often delay coding speech-language delay (SLD) diagnoses, with over a quarter of cases showing delayed coding. Natural language processing (NLP) can identify SLD concerns in clinical notes, aiding early detection.

Keywords:

Natural Language Processing electronic health records speech-language delay

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Last Updated: Jun 16, 2026

Portable Intermodal Preferential Looking (IPL): Investigating Language Comprehension in Typically Developing Toddlers and Young Children with Autism

Portable Intermodal Preferential Looking (IPL): Investigating Language Comprehension in Typically Developing Toddlers and Young Children with Autism

Published on: December 14, 2012

Practical Methodology of Cognitive Tasks Within a Navigational Assessment

Practical Methodology of Cognitive Tasks Within a Navigational Assessment

Published on: June 1, 2015

Area of Science:

Pediatric Health
Clinical Informatics
Developmental Pediatrics

Background:

Speech-language delay (SLD) is a common developmental condition identified in children.
Pediatricians frequently adopt a
watch and wait
approach, potentially delaying formal diagnosis and coding.
Understanding the gap between documented concerns and diagnostic coding is crucial for timely intervention.

Purpose of the Study:

To assess the discordance between documented concerns for SLD in clinical notes and the presence of associated ICD-10 diagnostic codes.
To investigate whether this discordance varies based on sociodemographic, clinical, and service utilization factors.
To evaluate the utility of a Natural Language Processing (NLP) model in identifying SLD concerns within clinical notes.

Main Methods:

A matched cohort of children with and without coded SLD was used to train a BioClinicalBERT NLP model.
The NLP model was applied to a population-based test set of well-child visits.
Analysis focused on encounters where SLD was mentioned in notes but lacked an ICD-10 code, examining factors associated with delayed coding.

Main Results:

The NLP model demonstrated high accuracy (AUCs of 0.98 and 0.99).
Only 52% of encounters with documented SLD concerns had an immediate ICD-10 code; 39% received a code later, indicating at least 26% delayed coding.
Younger children and those with private insurance were less likely to have timely coding; younger children with more outpatient visits were more likely to receive a future SLD diagnosis.

Conclusions:

NLP tools can effectively identify SLD concerns in clinical notes, facilitating the capture of early concerns.
The observed gaps between documentation and coding may reflect the
watch and wait
approach.
Future research on SLD should account for potential discordance between clinical documentation and diagnostic coding for accurate real-world data analysis.