A comprehensive causal and machine learning framework for autism spectrum disorder risk prediction and high-risk subgroup identification from children with chronic health and genetic disorders in the United States / Md Roungu AHMMAD in Research in Autism, 131 (March 2026)  

Public ISBD [article]  inResearch in Autism > 131 (March 2026) . - 202826 Titre : A comprehensive causal and machine learning framework for autism spectrum disorder risk prediction and high-risk subgroup identification from children with chronic health and genetic disorders in the United States Type de document : texte imprimé Auteurs : Md Roungu AHMMAD, Auteur ; Harry PANTAZOPOULOS, Auteur ; Sahil Hareshbhai KOTHIYA, Auteur Article en page(s) : 202826 Langues : Anglais (eng) Mots-clés : Autism Spectrum Disorder  Propensity Score Matching  Machine Learning  Chronic Disease  Genetic Disorder Index. décimale : PER Périodiques Résumé : Purpose Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition often accompanied by diverse comorbidities. This study aimed to develop and evaluate a machine learning framework to identify key predictors of ASD and characterize disease–disease relationships. Methods We analyzed 2022–2023 National Survey of Children’s Health (NSCH) data. Propensity score matching (PSM) was applied to reduce confounding and approximate a randomized controlled design. Multiple machine learning algorithms were implemented to identify important predictors and assess disease–disease interactions. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration plots, and cross-validation. Results In matched cohorts, epilepsy showed the strongest association with ASD (20.0 % vs. 6.1 %; OR = 3.85, 95 % CI: 2.88–5.20). Down syndrome (DS) (14.5 % vs. 4.7 %; OR = 3.45, 95 % CI: 1.75–7.30) and congenital heart disease (CHD) (7.5 % vs. 5.4 %; OR = 1.42, 95 % CI: 1.14–1.77) were also significant, whereas diabetes, cystic fibrosis, and allergies demonstrated no consistent associations. Across machine learning analyses, epilepsy consistently emerged as the top predictor, with DS and CHD also ranked highly. Children with both epilepsy and CHD represented the highest-risk subgroup (>32 %), while those without epilepsy but with both allergies and CHD had moderate risk (>10 %). Model discrimination was good (AUC = 0.771) with satisfactory calibration. Conclusion Epilepsy is the strongest predictor of ASD, with epilepsy - CHD comorbidity conferring the highest-risk subgroup, whereas no epilepsy but allergies and CHD represented moderate-risk groups. This framework highlights the value of advanced analytics for early identification and targeted interventions in vulnerable populations. En ligne : https://doi.org/10.1016/j.reia.2026.202826 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=581 [article] A comprehensive causal and machine learning framework for autism spectrum disorder risk prediction and high-risk subgroup identification from children with chronic health and genetic disorders in the United States [texte imprimé] / Md Roungu AHMMAD, Auteur ; Harry PANTAZOPOULOS, Auteur ; Sahil Hareshbhai KOTHIYA, Auteur . - 202826. Langues : Anglais (eng) in Research in Autism > 131 (March 2026) . - 202826 Mots-clés : Autism Spectrum Disorder  Propensity Score Matching  Machine Learning  Chronic Disease  Genetic Disorder Index. décimale : PER Périodiques Résumé : Purpose Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition often accompanied by diverse comorbidities. This study aimed to develop and evaluate a machine learning framework to identify key predictors of ASD and characterize disease–disease relationships. Methods We analyzed 2022–2023 National Survey of Children’s Health (NSCH) data. Propensity score matching (PSM) was applied to reduce confounding and approximate a randomized controlled design. Multiple machine learning algorithms were implemented to identify important predictors and assess disease–disease interactions. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration plots, and cross-validation. Results In matched cohorts, epilepsy showed the strongest association with ASD (20.0 % vs. 6.1 %; OR = 3.85, 95 % CI: 2.88–5.20). Down syndrome (DS) (14.5 % vs. 4.7 %; OR = 3.45, 95 % CI: 1.75–7.30) and congenital heart disease (CHD) (7.5 % vs. 5.4 %; OR = 1.42, 95 % CI: 1.14–1.77) were also significant, whereas diabetes, cystic fibrosis, and allergies demonstrated no consistent associations. Across machine learning analyses, epilepsy consistently emerged as the top predictor, with DS and CHD also ranked highly. Children with both epilepsy and CHD represented the highest-risk subgroup (>32 %), while those without epilepsy but with both allergies and CHD had moderate risk (>10 %). Model discrimination was good (AUC = 0.771) with satisfactory calibration. Conclusion Epilepsy is the strongest predictor of ASD, with epilepsy - CHD comorbidity conferring the highest-risk subgroup, whereas no epilepsy but allergies and CHD represented moderate-risk groups. This framework highlights the value of advanced analytics for early identification and targeted interventions in vulnerable populations. En ligne : https://doi.org/10.1016/j.reia.2026.202826 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=581

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