ArticleEuropean psychiatry : the journal of the Association of European Psychiatrists2020
Identification of newborns at risk for autism using electronic medical records and machine learning.
Article in European psychiatry : the journal of the Association of European Psychiatrists, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- A Systematic Review of Wearable Sensors in Rett Syndrome-What Physiological Markers Are Informative for Monitoring Disease States?Sensors (Basel, Switzerland) · 2025Pooled it
- Health databases and early identification of autism spectrum disorder: a scoping review.BMJ open · 2026Article
- Harnessing artificial intelligence for pediatric health: Current trends and future opportunities.iScience · 2026Review
- A Brief Observation to Screen Autism in Toddlers and Predict Developmental Trajectory.Journal of pediatrics. Clinical practice · 2025Article
- Predicting autism spectrum disorder severity in children based on specific language milestones: a random forest model approach.Child and adolescent psychiatry and mental health · 2025Article
- Predicting 5-year-olds mental health at birth: development and internal validation of a multivariable model using the prospective ELFE birth cohort.European child & adolescent psychiatry · 2025Article
- [Artificial intelligence in preventive medicine for children and adolescents-applications and acceptance].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025Review
- Predicting the onset of mental health problems in adolescents.Psychological medicine · 2025Article
- The Role of Artificial Intelligence for Early Diagnostic Tools of Autism Spectrum Disorder: A Systematic Review.Turkish archives of pediatrics · 2025Article
- Age of machine learning: new trends in autism spectrum disorder prediction.Frontiers in microbiology · 2025Review
- 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
- Machine Learning Differentiation of Autism Spectrum Sub-Classifications.Journal of autism and developmental disorders · 2024Article
- A Prediction Model of Autism Spectrum Diagnosis from Well-Baby Electronic Data Using Machine Learning.Children (Basel, Switzerland) · 2024Article
- Assessment of autonomic symptom scales in patients with neurodevelopmental diagnoses using electronic health record data.Research in autism spectrum disorders · 2023Article
- Assessing Autistic Traits in Toddlers Using a Data-Driven Approach with DSM-5 Mapping.Bioengineering (Basel, Switzerland) · 2023Article
- The usefulness of machine-learning-based evaluation of clinical and pretreatmentThe British journal of radiology · 2023Article
- The Usefulness of Machine Learning-Based Evaluation of Clinical and Pretreatment [Molecular imaging and biology · 2023Article
- Machine learning based evaluation of clinical and pretreatmentAbdominal radiology (New York) · 2022Article
- Computational Models for Clinical Applications in Personalized Medicine-Guidelines and Recommendations for Data Integration and Model Validation.Journal of personalized medicine · 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 at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCurrent approaches for early identification of individuals at high risk for autism spectrum disorder (ASD) in the general population are limited, and most ASD patients are not identified until after the age of 4. This is despite substantial evidence suggesting that early diagnosis and intervention improves developmental course and outcome. The aim of the current study was to test the ability of machine learning (ML) models applied to electronic medical records (EMRs) to predict ASD early in life, in a general population sample.
methodsWe used EMR data from a single Israeli Health Maintenance Organization, including EMR information for parents of 1,397 ASD children (ICD-9/10) and 94,741 non-ASD children born between January 1st, 1997 and December 31st, 2008. Routinely available parental sociodemographic information, parental medical histories, and prescribed medications data were used to generate features to train various ML algorithms, including multivariate logistic regression, artificial neural networks, and random forest. Prediction performance was evaluated with 10-fold cross-validation by computing the area under the receiver operating characteristic curve (AUC; C-statistic), sensitivity, specificity, accuracy, false positive rate, and precision (positive predictive value [PPV]).
resultsAll ML models tested had similar performance. The average performance across all models had C-statistic of 0.709, sensitivity of 29.93%, specificity of 98.18%, accuracy of 95.62%, false positive rate of 1.81%, and PPV of 43.35% for predicting ASD in this dataset.
conclusionsWe conclude that ML algorithms combined with EMR capture early life ASD risk as well as reveal previously unknown features to be associated with ASD-risk. Such approaches may be able to enhance the ability for accurate and efficient early detection of ASD in large populations of children.
Indexed as
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.