ArticleJAMA network open2023
Predictive Value of Early Autism Detection Models Based on Electronic Health Record Data Collected Before Age 1 Year.
Article in JAMA network open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 20 citations in OpenAlex.
- Article
- Health databases and early identification of autism spectrum disorder: a scoping review.BMJ open · 2026Article
- Early attention deficit hyperactivity disorder prediction from longitudinal electronic health records.Nature. Mental health · 2026Article
- The role of machine learning in autism spectrum disorder assessment and management.Pediatric research · 2025Review
- Genetic and environmental influences in autism: guiding the future of tailored early detection and intervention.The Journal of clinical investigation · 2025Article
- Modeling psychopathology in high-dimensional vector space using the high-dimensional symptom space (HDSS) model can operationalize precision psychiatry in US adolescents.Scientific reports · 2025Article
- Borrowing From the Future: Enhancing Early Risk Assessment through Contrastive Learning.Proceedings of machine learning research · 2025Article
- Balancing Interpretability and Flexibility in Modeling Diagnostic Trajectories with an Embedded Neural Hawkes Process Model.Proceedings of machine learning research · 2025Article
- Assessment and ascertainment in psychiatric molecular genetics: challenges and opportunities for cross-disorder research.Molecular psychiatry · 2025Review
- Article
- Recent Developments in the Application of Artificial Intelligence and Machine Learning in Early Screening and Diagnosis of Autism.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field.Journal of neurodevelopmental disorders · 2024Review
- A conditional multi-label model to improve prediction of a rare outcome: An illustration predicting autism diagnosis.Journal of biomedical informatics · 2024Article
- Machine Learning Prediction of Autism Spectrum Disorder From a Minimal Set of Medical and Background Information.JAMA network open · 2024Article
- Identifying Precise Targets to Improve Child Mental Health Care Equity: Leveraging Advances in Clinical Research Informatics and Lived Experience.Child and adolescent psychiatric clinics of North America · 2024Review
- Early Prediction of Autistic Spectrum Disorder Using Developmental Surveillance Data.JAMA network open · 2024Article
- AI in Rehabilitation Medicine: Opportunities and Challenges.Annals of rehabilitation medicine · 2023Article
- Sex differences in the age of childhood autism diagnosis and the impact of co-occurring conditions.Autism research : official journal of the International Society for Autism Research · 2023Article
- Early detection of autism using digital behavioral phenotyping.Nature medicine · 2023Article
- Factors in Infancy That May Predict Autism Spectrum Disorder.Brain sciences · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 3 institutions in 1 country.
Funding
Abstract
Importance: Autism detection early in childhood is critical to ensure that autistic children and their families have access to early behavioral support. Early correlates of autism documented in electronic health records (EHRs) during routine care could allow passive, predictive model-based monitoring to improve the accuracy of early detection. Objective: To quantify the predictive value of early autism detection models based on EHR data collected before age 1 year. Design, Setting, and Participants: This retrospective diagnostic study used EHR data from children seen within the Duke University Health System before age 30 days between January 2006 and December 2020. These data were used to train and evaluate L2-regularized Cox proportional hazards models predicting later autism diagnosis based on data collected from birth up to the time of prediction (ages 30-360 days). Statistical analyses were performed between August 1, 2020, and April 1, 2022. Main Outcomes and Measures: Prediction performance was quantified in terms of sensitivity, specificity, and positive predictive value (PPV) at clinically relevant model operating thresholds. Results: Data from 45 080 children, including 924 (1.5%) meeting autism criteria, were included in this study. Model-based autism detection at age 30 days achieved 45.5% sensitivity and 23.0% PPV at 90.0% specificity. Detection by age 360 days achieved 59.8% sensitivity and 17.6% PPV at 81.5% specificity and 38.8% sensitivity and 31.0% PPV at 94.3% specificity. Conclusions and Relevance: In this diagnostic study of an autism screening test, EHR-based autism detection achieved clinically meaningful accuracy by age 30 days, improving by age 1 year. This automated approach could be integrated with caregiver surveys to improve the accuracy of early autism screening.
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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.