ArticleScience advances2021
Reduced false positives in autism screening via digital biomarkers inferred from deep comorbidity patterns.
Article in Science advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Computerized Adaptive Tests for Rapid and Accurate Assessment of Autism.JAMA network open · 2026Article
- Ethics in digital phenotyping: considerations regarding Alzheimer's disease, speech and artificial intelligence.Journal of medical ethics · 2025Article
- Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field.Journal of neurodevelopmental disorders · 2024Review
- A deep learning model for prediction of autism status using whole-exome sequencing data.PLoS computational biology · 2024Article
- Autism Spectrum Disorder in Children: Early Signs and Therapeutic Interventions.Children (Basel, Switzerland) · 2024Review
- Machine Learning Prediction of Autism Spectrum Disorder From a Minimal Set of Medical and Background Information.JAMA network open · 2024Article
- Early Prediction of Autistic Spectrum Disorder Using Developmental Surveillance Data.JAMA network open · 2024Article
- Brief Report: The Characterization of Medical Comorbidity Prior to Autism Diagnosis in Children Before Age Two.Journal of autism and developmental disorders · 2023Article
- Prediction of autism in infants: progress and challenges.The Lancet. Neurology · 2023Review
- Predictive Value of Early Autism Detection Models Based on Electronic Health Record Data Collected Before Age 1 Year.JAMA network open · 2023Article
- Screening for idiopathic pulmonary fibrosis using comorbidity signatures in electronic health records.Nature medicine · 2022Article
- Cardiac Comorbidity Risk Score: Zero-Burden Machine Learning to Improve Prediction of Postoperative Major Adverse Cardiac Events in Hip and Knee Arthroplasty.Journal of the American Heart Association · 2022Observational
- Digital Biomarkers in Psychiatric Research: Data Protection Qualifications in a Complex Ecosystem.Frontiers in psychiatry · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Here, we develop digital biomarkers for autism spectrum disorder (ASD), computed from patterns of past medical encounters, identifying children at high risk with an area under the receiver operating characteristic exceeding 80% from shortly after 2 years of age for either sex, and across two independent patient databases. We leverage uncharted ASD comorbidities, with no requirement of additional blood work, or procedures, to estimate the autism comorbid risk score (ACoR), during the earliest years when interventions are the most effective. ACoR has superior predictive performance to common questionnaire-based screenings and can reduce their current socioeconomic, ethnic, and demographic biases. In addition, we can condition on current screening scores to either halve the state-of-the-art false-positive rate or boost sensitivity to over 60%, while maintaining specificity above 95%. Thus, ACoR can significantly reduce the median diagnostic age, reducing diagnostic delays and accelerating access to evidence-based interventions.
Identifiers
What OpenQuestion holds
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.