Putting the child in the driver's seat: Insights into language development from children's interactions in preschool classrooms.

Children's own language production has a role in structuring the language of their conversation partners and influences their own development. Children's active participation in their own language development is most apparent in the rich body of work investigating language in natural environments. The advent of automated measures of vocalizations and movement have made such in situ research increasingly feasible. In this chapter, we review recent research on children's language development in context with a particular focus on research employing automated methods in preschool classrooms for children between ages 2 and 5 years. These automated methods indicate that the speech directed to preschool children from specific peers predicts the child's speech to those peers on a subsequent observation occasion. Similar patterns are seen in the influence of peer and teacher phonemic diversity on the phonemic diversity of children's speech to those partners. In both cases, children's own speech to partners was the best predictor of their language abilities, suggesting their active role in their own development. Finally, new research suggests the potential of machine learning to predict children's speech in group contexts, and to transcribe classroom speech to better understand the content of children's conversations and how they change with development.

Delay from Screening to Diagnosis in Autism Spectrum Disorder: Results from a Large National Health Research Network.

To examine delay from developmental screening to autism diagnosis, we used real-world health care data from a national research network to estimate the time between these events. We found an average delay of longer than 2 years from first screening to diagnosis, with no significant differences observed by sex, race, or ethnicity.

Prevalence of autism spectrum disorder in a large pediatric primary care network.

LAY ABSTRACT: Historically, children from non-Hispanic Black and Hispanic backgrounds, those from lower-income families, and girls are less likely to be diagnosed with autism spectrum disorder. Under-identification among these historically and contemporaneously marginalized groups can limit their access to early, autism spectrum disorder-specific interventions, which can have long-term negative impacts. Recent data suggest that some of these trends may be narrowing, or even reversing. Using electronic health record data, we calculated autism spectrum disorder prevalence rates and age of first documented diagnosis across socio-demographic groups. Our cohort included children seen at young ages (when eligible for screening in early childhood) and again at least after 4 years of age in a large primary care network. We found that autism spectrum disorder prevalence was unexpectedly higher among Asian children, non-Hispanic Black children, children with higher Social Vulnerability Index scores (a measure of socio-economic risk at the neighborhood level), and children who received care in urban primary care sites. We did not find differences in the age at which autism spectrum disorder diagnoses were documented in children's records across these groups. Receiving primary care at an urban site (regardless of location of specialty care) appeared to account for most other socio-demographic differences in autism spectrum disorder prevalence rates, except among Asian children, who remained more likely to be diagnosed with autism spectrum disorder after controlling for other factors. We must continue to better understand the process by which children with autism spectrum disorder from traditionally under-identified and under-served backgrounds come to be recognized, to continue to improve the equity of care.

Development of a phenotype ontology for autism spectrum disorder by natural language processing on electronic health records.

BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by restricted, repetitive behavior, and impaired social communication and interactions. However, significant challenges remain in diagnosing and subtyping ASD due in part to the lack of a validated, standardized vocabulary to characterize clinical phenotypic presentation of ASD. Although the human phenotype ontology (HPO) plays an important role in delineating nuanced phenotypes for rare genetic diseases, it is inadequate to capture characteristic of behavioral and psychiatric phenotypes for individuals with ASD. There is a clear need, therefore, for a well-established phenotype terminology set that can assist in characterization of ASD phenotypes from patients' clinical narratives. METHODS: To address this challenge, we used natural language processing (NLP) techniques to identify and curate ASD phenotypic terms from high-quality unstructured clinical notes in the electronic health record (EHR) on 8499 individuals with ASD, 8177 individuals with non-ASD psychiatric disorders, and 8482 individuals without a documented psychiatric disorder. We further performed dimensional reduction clustering analysis to subgroup individuals with ASD, using nonnegative matrix factorization method. RESULTS: Through a note-processing pipeline that includes several steps of state-of-the-art NLP approaches, we identified 3336 ASD terms linking to 1943 unique medical concepts, which represents among the largest ASD terminology set to date. The extracted ASD terms were further organized in a formal ontology structure similar to the HPO. Clustering analysis showed that these terms could be used in a diagnostic pipeline to differentiate individuals with ASD from individuals with other psychiatric disorders. CONCLUSION: Our ASD phenotype ontology can assist clinicians and researchers in characterizing individuals with ASD, facilitating automated diagnosis, and subtyping individuals with ASD to facilitate personalized therapeutic decision-making.