ArticlePediatric research2016
Pregnancy risk factors in autism: a pilot study with artificial neural networks.
Article in Pediatric research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 3 syntheses or guidelines pooled it, 37 citations in OpenAlex.
- Early Life Antibiotic Exposure and the Subsequent Risk of Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder: A Systematic Review and Meta-Analysis.Journal of autism and developmental disorders · 2022Pooled it
- Prenatal maternal stress and risk of neurodevelopmental disorders in the offspring: a systematic review and meta-analysis.Social psychiatry and psychiatric epidemiology · 2019Pooled it
- The Association between Autism Spectrum Disorder and Pre- and Postnatal Antibiotic Exposure in Childhood-A Systematic Review with Meta-Analysis.International journal of environmental research and public health · 2019Pooled it
- Article
- Environmental factors associated with autism spectrum disorder in Southern Europe: a systematic review.International journal of developmental disabilities · 2025Review
- Age of machine learning: new trends in autism spectrum disorder prediction.Frontiers in microbiology · 2025Review
- Not breastfeeding and risk of autism spectrum disorders among children: a meta-analysis.Clinical and experimental pediatrics · 2023Article
- Application of Artificial Intelligence in Screening for Adverse Perinatal Outcomes-A Systematic Review.Healthcare (Basel, Switzerland) · 2022Review
- Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements.Seminars in pediatric neurology · 2020Review
- Application of Artificial Neural Network for Prediction of Risk of Multiple Sclerosis Based on Single Nucleotide Polymorphism Genotypes.Journal of molecular neuroscience : MN · 2020Article
- Self-Reported Autistic Traits Using the AQ: A Comparison between Individuals with ASD, Psychosis, and Non-Clinical Controls.Brain sciences · 2020Article
- Application of Single-Nucleotide Polymorphisms in the Diagnosis of Autism Spectrum Disorders: A Preliminary Study with Artificial Neural Networks.Journal of molecular neuroscience : MN · 2019Article
- Association between hypertensive disorders of pregnancy and risk of autism in offspring: a systematic review and meta-analysis of observational studies.Oncotarget · 2018Article
- Interplay between maternal Slc6a4 mutation and prenatal stress: a possible mechanism for autistic behavior development.Scientific reports · 2017Article
- Does prenatal stress alter the developing connectome?Pediatric research · 2017Review
- Applying machine learning to identify autistic adults using imitation: An exploratory study.PloS one · 2017Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAutism is a multifactorial condition in which a single risk factor can unlikely provide comprehensive explanation for the disease origin. Moreover, due to the complexity of risk factors interplay, traditional statistics is often unable to explain the core of the problem due to the strong inherent nonlinearity of relationships. The aim of this study was to assess the frequency of 27 potential risk factors related to pregnancy and peri-postnatal period.
methodsThe mothers of 45 autistic children and of 68 typical developing children completed a careful interview. Twenty-four siblings of 19 autistic children formed an internal control group.
resultsA higher prevalence of potential risk factors was observed in 22 and 15 factors in external control and internal control groups, respectively. For six of them, the difference in prevalence was statistically significant. Specialized artificial neural networks (ANNs) discriminated between autism and control subjects with 80.19% global accuracy when the dataset was preprocessed with TWIST (training with input selection and testing) system selecting 16 out of 27 variables. Logistic regression applied to 27 variables gave unsatisfactory results with global accuracy of 46%.
conclusionPregnancy factors play an important role in autism development. ANNs are able to build up a predictive model, which could represent the basis for a diagnostic screening tool.
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