ArticleScientific reports2020
Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 85 citations in OpenAlex.
- Peer Interaction in Late-Diagnosed Autistic Adolescent Boys and Girls.Journal of clinical medicine · 2026Article
- Machine learning-based prediction of intellectual disability in children with autism spectrum disorder: using behavioral observation techniques.BMC psychiatry · 2026Article
- A Brief Observation to Screen Autism in Toddlers and Predict Developmental Trajectory.Journal of pediatrics. Clinical practice · 2025Article
- Compact subsets of autism screening items predict clinical diagnoses with a machine learning analysis of the QCHAT-10.Scientific reports · 2025Article
- ADOS-2 Module 4: Psychometric Properties and Diagnostic Performance at an Autism-specialized Clinic.Journal of autism and developmental disorders · 2025Article
- Predictive modeling of adaptive behavior trajectories in autism: insights from a clinical cohort study.Translational psychiatry · 2025Article
- Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities.Life (Basel, Switzerland) · 2024Review
- Modified Meta Heuristic BAT with ML Classifiers for Detection of Autism Spectrum Disorder.Biomolecules · 2023Article
- A Machine Learning Approach to the Diagnosis of Autism Spectrum Disorder and Multi-Systemic Developmental Disorder Based on Retrospective Data and ADOS-2 Score.Brain sciences · 2023Article
- Autism Spectrum Disorders in Adulthood-Symptoms, Diagnosis, and Treatment.Deutsches Arzteblatt international · 2023Review
- Extending artificial intelligence research in the clinical domain: a theoretical perspective.Annals of operations research · 2022Article
- A data driven machine learning approach to differentiate between autism spectrum disorder and attention-deficit/hyperactivity disorder based on the best-practice diagnostic instruments for autism.Scientific reports · 2022Article
- Abilities and Disabilities-Applying Machine Learning to Disentangle the Role of Intelligence in Diagnosing Autism Spectrum Disorders.Frontiers in psychiatry · 2022Article
- Hybrid Diagnosis Models for Autism Patients Based on Medical and Sociodemographic Features Using Machine Learning and Multicriteria Decision-Making (MCDM) Techniques: An Evaluation and Benchmarking Framework.Computational and mathematical methods in medicine · 2022Article
- Diagnosis-Based Hybridization of Multimedical Tests and Sociodemographic Characteristics of Autism Spectrum Disorder Using Artificial Intelligence and Machine Learning Techniques: A Systematic Review.International journal of telemedicine and applications · 2022Review
- Informing Developmental Milestone Achievement for Children With Autism: Machine Learning Approach.JMIR medical informatics · 2021Article
- A Systematic Literature Review on the Application of Machine-Learning Models in Behavioral Assessment of Autism Spectrum Disorder.Journal of personalized medicine · 2021Review
- Is the Combination of ADOS and ADI-R Necessary to Classify ASD? Rethinking the "Gold Standard" in Diagnosing ASD.Frontiers in psychiatry · 2021Article
- Identifying Subgroups of Patients With Autism by Gene Expression Profiles Using Machine Learning Algorithms.Frontiers in psychiatry · 2021Article
- Feature replacement methods enable reliable home video analysis for machine learning detection of autism.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
11 authors at 7 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diagnosing autism spectrum disorders (ASD) is a complicated, time-consuming process which is particularly challenging in older individuals. One of the most widely used behavioral diagnostic tools is the Autism Diagnostic Observation Schedule (ADOS). Previous work using machine learning techniques suggested that ASD detection in children can be achieved with substantially fewer items than the original ADOS. Here, we expand on this work with a specific focus on adolescents and adults as assessed with the ADOS Module 4. We used a machine learning algorithm (support vector machine) to examine whether ASD detection can be improved by identifying a subset of behavioral features from the ADOS Module 4 in a routine clinical sample of N = 673 high-functioning adolescents and adults with ASD (n = 385) and individuals with suspected ASD but other best-estimate or no psychiatric diagnoses (n = 288). We identified reduced subsets of 5 behavioral features for the whole sample as well as age subgroups (adolescents vs. adults) that showed good specificity and sensitivity and reached performance close to that of the existing ADOS algorithm and the full ADOS, with no significant differences in overall performance. These results may help to improve the complicated diagnostic process of ASD by encouraging future efforts to develop novel diagnostic instruments for ASD detection based on the identified constructs as well as aiding clinicians in the difficult question of differential diagnosis.
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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.