ArticlePloS one2016
Development of a Machine Learning Algorithm for the Surveillance of Autism Spectrum Disorder.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Applying artificial intelligence in neurodevelopmental disorders management and research.European journal of medical research · 2026Review
- The role of machine learning in autism spectrum disorder assessment and management.Pediatric research · 2025Review
- An Updated Characterization of Childhood Selective Mutism: Exploring Clinical Features, Treatment Utilization, and School Services.Child psychiatry and human development · 2025Article
- Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field.Journal of neurodevelopmental disorders · 2024Review
- Contributions of Artificial Intelligence to Analysis of Gut Microbiota in Autism Spectrum Disorder: A Systematic Review.Children (Basel, Switzerland) · 2024Review
- Transparent deep learning to identify autism spectrum disorders (ASD) in EHR using clinical notes.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- A Prediction Model of Autism Spectrum Diagnosis from Well-Baby Electronic Data Using Machine Learning.Children (Basel, Switzerland) · 2024Article
- A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism.Annual review of biomedical data science · 2023Review
- Pediatric Injury Surveillance From Uncoded Emergency Department Admission Records in Italy: Machine Learning-Based Text-Mining Approach.JMIR public health and surveillance · 2023Article
- Sensory features in autism: Findings from a large population-based surveillance system.Autism research : official journal of the International Society for Autism Research · 2022Article
- Promising Practices in the Frontiers of Quality Outcome Measurement for Intellectual and Developmental Disability Services.Frontiers in rehabilitation sciences · 2022Article
- Comparison of 2 Case Definitions for Ascertaining the Prevalence of Autism Spectrum Disorder Among 8-Year-Old Children.American journal of epidemiology · 2021Article
- Informing Developmental Milestone Achievement for Children With Autism: Machine Learning Approach.JMIR medical informatics · 2021Article
- Is the Combination of ADOS and ADI-R Necessary to Classify ASD? Rethinking the "Gold Standard" in Diagnosing ASD.Frontiers in psychiatry · 2021Article
- Virulence factor-related gut microbiota genes and immunoglobulin A levels as novel markers for machine learning-based classification of autism spectrum disorder.Computational and structural biotechnology journal · 2021Article
- Autism spectrum disorder/Takiwātanga: An Integrated Data Infrastructure-based approach to autism spectrum disorder research in New Zealand.Autism : the international journal of research and practice · 2020Article
- Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning.Scientific reports · 2020Article
- Review of Machine Learning Algorithms for Diagnosing Mental Illness.Psychiatry investigation · 2019Article
- Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment.JMIR public health and surveillance · 2019Article
- A comparison of machine learning algorithms for the surveillance of autism spectrum disorder.PloS one · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Autism and Developmental Disabilities Monitoring (ADDM) Network conducts population-based surveillance of autism spectrum disorder (ASD) among 8-year old children in multiple US sites. To classify ASD, trained clinicians review developmental evaluations collected from multiple health and education sources to determine whether the child meets the ASD surveillance case criteria. The number of evaluations collected has dramatically increased since the year 2000, challenging the resources and timeliness of the surveillance system. We developed and evaluated a machine learning approach to classify case status in ADDM using words and phrases contained in children's developmental evaluations. We trained a random forest classifier using data from the 2008 Georgia ADDM site which included 1,162 children with 5,396 evaluations (601 children met ADDM ASD criteria using standard ADDM methods). The classifier used the words and phrases from the evaluations to predict ASD case status. We evaluated its performance on the 2010 Georgia ADDM surveillance data (1,450 children with 9,811 evaluations; 754 children met ADDM ASD criteria). We also estimated ASD prevalence using predictions from the classification algorithm. Overall, the machine learning approach predicted ASD case statuses that were 86.5% concordant with the clinician-determined case statuses (84.0% sensitivity, 89.4% predictive value positive). The area under the resulting receiver-operating characteristic curve was 0.932. Algorithm-derived ASD "prevalence" was 1.46% compared to the published (clinician-determined) estimate of 1.55%. Using only the text contained in developmental evaluations, a machine learning algorithm was able to discriminate between children that do and do not meet ASD surveillance criteria at one surveillance site.
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Registered trials
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